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Document Review AccuracyRisk MitigationSpeed to Strategy and CompletionCost of eDiscoveryFinding the Right AI for the JobLearn more
Insights

AI Is All the Rage — But What’s the ROI in eDiscovery?

Not All AI is Created Equally  

The eDiscovery market is suddenly crowded with AI tools and platforms. It makes sense—AI is perfectly suited for the large datasets, rule-based analysis, and need for speed and efficiency that define modern document review.

But not all AI tools are created equally—so how do you sort through the noise to find the solutions best fit for you? What’s most important? The latest, greatest tech or what’s tried and true? At the end of the day, those aren’t the most important questions to consider.

Instead, here are three questions you need to answer right away:

What is my goal?
How Is AI uniquely suited to help me?  
What are the measures of success?

These questions will help you look beyond the “made with AI” labels and find solutions that make a real difference on your work and bottom line.  

To get you started, here are four ways that our clients have seen AI add value in eDiscovery.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

AI Improves Document Review Deliverables and Timelines

Studies have shown that machine learning tools from a decade ago are at least as reliable as human reviewers—and today’s AI tools are even better. Lighthouse has proven this in real-world, head-to-head comparisons between our modern AI and other review tools (see examples below).

Analytic tools built with AI, such as large language models (LLMs), do a better job of detecting privilege, personally identifiable information, confidential information, and junk data. This saves a wealth of time and trouble down the line, through fewer downstream tasks like privilege review, redactions, and foreign language translation. It also significantly lowers the odds of disclosing non-relevant but sensitive information that could fuel more litigation.

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

No / Old AI

  • Words evaluated individually, at face value
  • Analysis limited to text
  • Broad analysis pulls in irrelevant docs for review
  • Variable efficacy, highly dependent on document richness and training docs

Modern AI

  • Words evaluated in context, accounting for different usages/meanings
  • Analysis includes text, metadata, and other data types
  • Nuanced analysis pulls in fewer irrelevant docs for review
  • Specific base models for each classification type leads to more accurate analytic results

‍

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

No / Old AI

  • Words evaluated individually, at face value
  • Analysis limited to text
  • Broad analysis pulls in irrelevant docs for review
  • Variable efficacy, highly dependent on document richness and training docs

Modern AI

  • Words evaluated in context, accounting for different usages/meanings
  • Analysis includes text, metadata, and other data types
  • Nuanced analysis pulls in fewer irrelevant docs for review
  • Specific base models for each classification type leads to more accurate analytic results

‍

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

No / Old AI

  • Words evaluated individually, at face value
  • Analysis limited to text
  • Broad analysis pulls in irrelevant docs for review
  • Variable efficacy, highly dependent on document richness and training docs

Modern AI

  • Words evaluated in context, accounting for different usages/meanings
  • Analysis includes text, metadata, and other data types
  • Nuanced analysis pulls in fewer irrelevant docs for review
  • Specific base models for each classification type leads to more accurate analytic results

‍

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

No / Old AI

  • Words evaluated individually, at face value
  • Analysis limited to text
  • Broad analysis pulls in irrelevant docs for review
  • Variable efficacy, highly dependent on document richness and training docs

Modern AI

  • Words evaluated in context, accounting for different usages/meanings
  • Analysis includes text, metadata, and other data types
  • Nuanced analysis pulls in fewer irrelevant docs for review
  • Specific base models for each classification type leads to more accurate analytic results

‍

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

No / Old AI

  • Words evaluated individually, at face value
  • Analysis limited to text
  • Broad analysis pulls in irrelevant docs for review
  • Variable efficacy, highly dependent on document richness and training docs

Modern AI

  • Words evaluated in context, accounting for different usages/meanings
  • Analysis includes text, metadata, and other data types
  • Nuanced analysis pulls in fewer irrelevant docs for review
  • Specific base models for each classification type leads to more accurate analytic results

‍

Lighthouse AI Results in Smaller, More Precise Responsive Sets*

During review for a Hart-Scott-Rodino Second Request, counsel ran the same documents through 3 different TAR models (Lighthouse AI, Relativity, and Brainspace) with the same training documents and parameters.

*Data shown is for 70% recall.

308K

Fewer documents than Relativity; ~94K fewer than Brainspace


89%

Precision, compared to 73% for Relativity and 83% for Brainspace

Lighthouse AI Outperforms Priv Terms

In a matter with 1.5 million documents, a client compared the efficacy of Lighthouse AI and privilege terms. The percentage of potential privilege identified by each method was measured against families withheld or redacted for privilege.

6.6x

Lighthouse AI was more than six times more accurate than search terms in finding potential privilege families

No / Old AI

  • Words evaluated individually, at face value
  • Analysis limited to text
  • Broad analysis pulls in irrelevant docs for review
  • Variable efficacy, highly dependent on document richness and training docs

Modern AI

  • Words evaluated in context, accounting for different usages/meanings
  • Analysis includes text, metadata, and other data types
  • Nuanced analysis pulls in fewer irrelevant docs for review
  • Specific base models for each classification type leads to more accurate analytic results

‍

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

AI Mitigates Risk Through Data Reuse and Trend Analysis

The accuracy of AI is one way it lowers risk. Another way is by applying knowledge across matters: Once a document is classified for one matter, reviewers can see how it was coded previously and make the same classification in current and future matters. This makes it much less likely that you’ll produce sensitive and privileged information to investigators and opposing counsel.  

Additionally, AI analytics are accessible in a dashboard view of an organization’s entire legal portfolio, helping teams identify risk trends they wouldn’t see otherwise. For example, analytics might show a higher incidence of litigation across certain custodians or a trend of outdated material stored in certain data sources.

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

No / Old AI

  • Search terms miss too many priv and sensitive docs
  • Search terms cannot show historical coding
  • Docs may be coded differently across matters, increasing risk of producing sensitive or priv docs
  • QC relies on the same type of analysis as initial review (i.e., more humans)

Modern AI

  • Nuanced search finds more priv and sensitive docs
  • Historical coding insights help reviewers with consistency
  • Coding can be reused, increasing consistency and lowering risk
  • QC bolstered by statistical analysis; discrepancies between AI and attorney judgments indicate a need for more scrutiny

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

No / Old AI

  • Search terms miss too many priv and sensitive docs
  • Search terms cannot show historical coding
  • Docs may be coded differently across matters, increasing risk of producing sensitive or priv docs
  • QC relies on the same type of analysis as initial review (i.e., more humans)

Modern AI

  • Nuanced search finds more priv and sensitive docs
  • Historical coding insights help reviewers with consistency
  • Coding can be reused, increasing consistency and lowering risk
  • QC bolstered by statistical analysis; discrepancies between AI and attorney judgments indicate a need for more scrutiny

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

No / Old AI

  • Search terms miss too many priv and sensitive docs
  • Search terms cannot show historical coding
  • Docs may be coded differently across matters, increasing risk of producing sensitive or priv docs
  • QC relies on the same type of analysis as initial review (i.e., more humans)

Modern AI

  • Nuanced search finds more priv and sensitive docs
  • Historical coding insights help reviewers with consistency
  • Coding can be reused, increasing consistency and lowering risk
  • QC bolstered by statistical analysis; discrepancies between AI and attorney judgments indicate a need for more scrutiny

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000
A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

No / Old AI

  • Search terms miss too many priv and sensitive docs
  • Search terms cannot show historical coding
  • Docs may be coded differently across matters, increasing risk of producing sensitive or priv docs
  • QC relies on the same type of analysis as initial review (i.e., more humans)

Modern AI

  • Nuanced search finds more priv and sensitive docs
  • Historical coding insights help reviewers with consistency
  • Coding can be reused, increasing consistency and lowering risk
  • QC bolstered by statistical analysis; discrepancies between AI and attorney judgments indicate a need for more scrutiny

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

No / Old AI

  • Search terms miss too many priv and sensitive docs
  • Search terms cannot show historical coding
  • Docs may be coded differently across matters, increasing risk of producing sensitive or priv docs
  • QC relies on the same type of analysis as initial review (i.e., more humans)

Modern AI

  • Nuanced search finds more priv and sensitive docs
  • Historical coding insights help reviewers with consistency
  • Coding can be reused, increasing consistency and lowering risk
  • QC bolstered by statistical analysis; discrepancies between AI and attorney judgments indicate a need for more scrutiny

Lighthouse AI Powers Consistency in Privilege Review

A global pharmaceutical company used Lighthouse AI on a group of related matters. This enabled the company to reuse a total of 26K previous privilege coding decisions, avoiding inadvertent disclosures and heading off potential challenges from opposing counsel.

Case A Case B Case C Case D Case E
Reused priv coding 4,300 6,080 970 4,100 11,000

No / Old AI

  • Search terms miss too many priv and sensitive docs
  • Search terms cannot show historical coding
  • Docs may be coded differently across matters, increasing risk of producing sensitive or priv docs
  • QC relies on the same type of analysis as initial review (i.e., more humans)

Modern AI

  • Nuanced search finds more priv and sensitive docs
  • Historical coding insights help reviewers with consistency
  • Coding can be reused, increasing consistency and lowering risk
  • QC bolstered by statistical analysis; discrepancies between AI and attorney judgments indicate a need for more scrutiny

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

AI Empowers with Early Insights and Faster Workflows

Enhancements in AI technology in recent years have led to tools that work faster even when dealing with large datasets. They provide a clearer view of matters at an earlier stage in the game, so you can make more informed legal and review strategy decisions right from the outset. They also get you to the end of document review more quickly, so you can avoid last-minute sprints and spend more time building your case.

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

No / Old AI

  • Earliest insights emerge weeks to months into doc review
  • Responsive review and priv review must happen in sequence
  • Responsive model goes back to start if the dataset changes
  • False negatives lead to surprises in later stages
  • QC spends more time managing review and checking work
  • Review drags on for months

Modern AI

  • Initial insights available within days for faster case assessment and data-backed case strategy
  • Responsive review and priv review can happen simultaneously
  • Responsive models adapt to dataset changes
  • No surprises
  • QC has more time to assess the substance of docs
  • Review completed in less time

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

No / Old AI

  • Earliest insights emerge weeks to months into doc review
  • Responsive review and priv review must happen in sequence
  • Responsive model goes back to start if the dataset changes
  • False negatives lead to surprises in later stages
  • QC spends more time managing review and checking work
  • Review drags on for months

Modern AI

  • Initial insights available within days for faster case assessment and data-backed case strategy
  • Responsive review and priv review can happen simultaneously
  • Responsive models adapt to dataset changes
  • No surprises
  • QC has more time to assess the substance of docs
  • Review completed in less time

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

No / Old AI

  • Earliest insights emerge weeks to months into doc review
  • Responsive review and priv review must happen in sequence
  • Responsive model goes back to start if the dataset changes
  • False negatives lead to surprises in later stages
  • QC spends more time managing review and checking work
  • Review drags on for months

Modern AI

  • Initial insights available within days for faster case assessment and data-backed case strategy
  • Responsive review and priv review can happen simultaneously
  • Responsive models adapt to dataset changes
  • No surprises
  • QC has more time to assess the substance of docs
  • Review completed in less time

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments
A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

No / Old AI

  • Earliest insights emerge weeks to months into doc review
  • Responsive review and priv review must happen in sequence
  • Responsive model goes back to start if the dataset changes
  • False negatives lead to surprises in later stages
  • QC spends more time managing review and checking work
  • Review drags on for months

Modern AI

  • Initial insights available within days for faster case assessment and data-backed case strategy
  • Responsive review and priv review can happen simultaneously
  • Responsive models adapt to dataset changes
  • No surprises
  • QC has more time to assess the substance of docs
  • Review completed in less time

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

No / Old AI

  • Earliest insights emerge weeks to months into doc review
  • Responsive review and priv review must happen in sequence
  • Responsive model goes back to start if the dataset changes
  • False negatives lead to surprises in later stages
  • QC spends more time managing review and checking work
  • Review drags on for months

Modern AI

  • Initial insights available within days for faster case assessment and data-backed case strategy
  • Responsive review and priv review can happen simultaneously
  • Responsive models adapt to dataset changes
  • No surprises
  • QC has more time to assess the substance of docs
  • Review completed in less time

Lighthouse AI Crushes CAL for Early Insights

Case planning and strategy hinge on how soon you can assess responsiveness and privilege. Standard workflows for advanced AI from Lighthouse are orders of magnitude faster than traditional CAL models.

Dataset: 2M docs Building the responsive set Detecting sensitive info
CAL and Regex 8 weeks 8+ weeks
Lighthouse AI 15 Days including 2 weeks to train and 24 hours to produce probability assessments (highly likely, highly unlikely, etc.) 24 hours for arrival of first probability assessments

No / Old AI

  • Earliest insights emerge weeks to months into doc review
  • Responsive review and priv review must happen in sequence
  • Responsive model goes back to start if the dataset changes
  • False negatives lead to surprises in later stages
  • QC spends more time managing review and checking work
  • Review drags on for months

Modern AI

  • Initial insights available within days for faster case assessment and data-backed case strategy
  • Responsive review and priv review can happen simultaneously
  • Responsive models adapt to dataset changes
  • No surprises
  • QC has more time to assess the substance of docs
  • Review completed in less time

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

AI Lowers eDiscovery Spend

The accuracy, risk mitigation, and speed of advanced AI tools and analytics add up to less eyes-on review, faster timelines, and lower overall costs.

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

No / Old AI

  • Excessive eyes-on review requires more attorneys and higher costs
  • Doc review starts fresh with each matter
  • Lower accuracy of analytics means more downstream review and associated costs
  • ROI limited by document thresholds and capacity for structured data only

Modern AI

  • Eyes-on review can be strategically limited and assigned based on data that requires human decision making
  • Doc review informed and reduced by past decisions and insights
  • Higher accuracy decreases downstream review and associated costs
  • ROI enhanced by capacity for an astronomical number of datapoints across structured and unstructured data

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

No / Old AI

  • Excessive eyes-on review requires more attorneys and higher costs
  • Doc review starts fresh with each matter
  • Lower accuracy of analytics means more downstream review and associated costs
  • ROI limited by document thresholds and capacity for structured data only

Modern AI

  • Eyes-on review can be strategically limited and assigned based on data that requires human decision making
  • Doc review informed and reduced by past decisions and insights
  • Higher accuracy decreases downstream review and associated costs
  • ROI enhanced by capacity for an astronomical number of datapoints across structured and unstructured data

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

No / Old AI

  • Excessive eyes-on review requires more attorneys and higher costs
  • Doc review starts fresh with each matter
  • Lower accuracy of analytics means more downstream review and associated costs
  • ROI limited by document thresholds and capacity for structured data only

Modern AI

  • Eyes-on review can be strategically limited and assigned based on data that requires human decision making
  • Doc review informed and reduced by past decisions and insights
  • Higher accuracy decreases downstream review and associated costs
  • ROI enhanced by capacity for an astronomical number of datapoints across structured and unstructured data

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

No / Old AI

  • Excessive eyes-on review requires more attorneys and higher costs
  • Doc review starts fresh with each matter
  • Lower accuracy of analytics means more downstream review and associated costs
  • ROI limited by document thresholds and capacity for structured data only

Modern AI

  • Eyes-on review can be strategically limited and assigned based on data that requires human decision making
  • Doc review informed and reduced by past decisions and insights
  • Higher accuracy decreases downstream review and associated costs
  • ROI enhanced by capacity for an astronomical number of datapoints across structured and unstructured data

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

No / Old AI

  • Excessive eyes-on review requires more attorneys and higher costs
  • Doc review starts fresh with each matter
  • Lower accuracy of analytics means more downstream review and associated costs
  • ROI limited by document thresholds and capacity for structured data only

Modern AI

  • Eyes-on review can be strategically limited and assigned based on data that requires human decision making
  • Doc review informed and reduced by past decisions and insights
  • Higher accuracy decreases downstream review and associated costs
  • ROI enhanced by capacity for an astronomical number of datapoints across structured and unstructured data

Lighthouse AI Trims $1M Off Privilege Review Costs

In a recent matter, Lighthouse AI rated 208K documents from the responsive set “highly unlikely” to be privileged. Rather than using 1L reviewers to verify this determination on all 208K documents, counsel forwarded these docs directly to QC and production. In QC, reviewers agreed with Lighthouse AI’s assessment 99.1% of the time.

208K

Docs removed from priv review

=

$1.24M

in total savings*

*Based on human review at a rate of 25 docs/hr and $150/hr per reviewer.

Lighthouse AI Significantly Reduces Eyes-On Review

The superior accuracy of Lighthouse AI helped outside counsel reduce eyes-on review by identifying a smaller responsive set, removing thousands of irrelevant foreign-language documents, and targeting privilege docs more precisely. In terms of privilege, using AI instead of privilege terms avoided 18K additional hours of review.

“My team saved the client $4 million in document review and translation costs vs. what we would have spent had we used Brainspace or Relativity Analytics.”

— Head of eDiscovery innovation, Am Law 100 firm

‍

No / Old AI

  • Excessive eyes-on review requires more attorneys and higher costs
  • Doc review starts fresh with each matter
  • Lower accuracy of analytics means more downstream review and associated costs
  • ROI limited by document thresholds and capacity for structured data only

Modern AI

  • Eyes-on review can be strategically limited and assigned based on data that requires human decision making
  • Doc review informed and reduced by past decisions and insights
  • Higher accuracy decreases downstream review and associated costs
  • ROI enhanced by capacity for an astronomical number of datapoints across structured and unstructured data

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

A female lawyer looking out the window, holding a tablet.
A man and a woman looking at a tablet in an office building.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

Finding the Right AI for the Job

Examples and comparisons like these clarify how AI can make a material difference in areas that matter most to you—as long as it’s the right AI.  

How can you tell whether an AI solution can help you accomplish your goals? Look for key attributes like:

  • Large language models (LLMs) – LLMs are what enable the nuanced, context-conscious searches that make modern AI so accurate.
  • Predictive AI – This is a type of LLM that makes predictions about responsiveness, privilege, and other classifications with the goal of precision.
  • Deep learning – This is the latest iteration of how AI gets smarter with use; it’s far more sophisticated than machine learning, which is an earlier iteration still used by many tools on the market.

If you find AI terminology confusing, you’re not alone. Check out this infographic that provides simple, practical explanations.

And for more information about AI designed with ROI in mind, visit our AI and analytics page.

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