Uncover hidden insights

AI Multi-Document Correlation Agent

Connect the dots. Find the alpha.

Delegate your deepest research to a specialized AI agent. It reads across your entire document universe—contracts, financials, reports, emails—to find the subtle correlations, patterns, and contradictions that lead to breakthrough insights and a true information advantage.

Ideal for

Hedge Funds

Due Diligence Teams

Researchers

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Multi-Document Correlation Agent in action

Play video

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Multi-Document Correlation Agent in action

Play video

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Multi-Document Correlation Agent in action

Play video

Diligence Consistency Check

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    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Multi-Document Correlation Agent in action

Play video

Time comparison

Time comparison

Traditional way

Often Impossible Manually

With V7 Go agents

Hours

Average time saved

95%

Why V7 Go

Why V7 Go

Pattern & Trend Detection

Analyzes data across thousands of documents to identify recurring themes, patterns, and trends that would be impossible for a human to spot, such as a specific contract clause that consistently leads to disputes.

Pattern & Trend Detection

Analyzes data across thousands of documents to identify recurring themes, patterns, and trends that would be impossible for a human to spot, such as a specific contract clause that consistently leads to disputes.

Pattern & Trend Detection

Analyzes data across thousands of documents to identify recurring themes, patterns, and trends that would be impossible for a human to spot, such as a specific contract clause that consistently leads to disputes.

Pattern & Trend Detection

Analyzes data across thousands of documents to identify recurring themes, patterns, and trends that would be impossible for a human to spot, such as a specific contract clause that consistently leads to disputes.

Contradiction & Inconsistency Flagging

Acts as an automated auditor, cross-referencing information between multiple sources to find and flag contradictions, such as a discrepancy between a company's marketing claims and its SEC filings.

Contradiction & Inconsistency Flagging

Acts as an automated auditor, cross-referencing information between multiple sources to find and flag contradictions, such as a discrepancy between a company's marketing claims and its SEC filings.

Contradiction & Inconsistency Flagging

Acts as an automated auditor, cross-referencing information between multiple sources to find and flag contradictions, such as a discrepancy between a company's marketing claims and its SEC filings.

Contradiction & Inconsistency Flagging

Acts as an automated auditor, cross-referencing information between multiple sources to find and flag contradictions, such as a discrepancy between a company's marketing claims and its SEC filings.

Relationship & Network Mapping

Identifies and maps the relationships between entities mentioned across your documents, helping you visualize connections between people, companies, and projects.

Relationship & Network Mapping

Identifies and maps the relationships between entities mentioned across your documents, helping you visualize connections between people, companies, and projects.

Relationship & Network Mapping

Identifies and maps the relationships between entities mentioned across your documents, helping you visualize connections between people, companies, and projects.

Relationship & Network Mapping

Identifies and maps the relationships between entities mentioned across your documents, helping you visualize connections between people, companies, and projects.

Causal Inference Analysis

Surfaces potential cause-and-effect relationships by finding correlations between events and outcomes across your datasets, such as a new product launch and a subsequent spike in support tickets.

Causal Inference Analysis

Surfaces potential cause-and-effect relationships by finding correlations between events and outcomes across your datasets, such as a new product launch and a subsequent spike in support tickets.

Causal Inference Analysis

Surfaces potential cause-and-effect relationships by finding correlations between events and outcomes across your datasets, such as a new product launch and a subsequent spike in support tickets.

Causal Inference Analysis

Surfaces potential cause-and-effect relationships by finding correlations between events and outcomes across your datasets, such as a new product launch and a subsequent spike in support tickets.

Builds on Your Knowledge Hub

Connects your document set to a V7 Go Knowledge Hub of past research, allowing the agent to find correlations not just within the current project, but across your firm's entire history.

Builds on Your Knowledge Hub

Connects your document set to a V7 Go Knowledge Hub of past research, allowing the agent to find correlations not just within the current project, but across your firm's entire history.

Builds on Your Knowledge Hub

Connects your document set to a V7 Go Knowledge Hub of past research, allowing the agent to find correlations not just within the current project, but across your firm's entire history.

Builds on Your Knowledge Hub

Connects your document set to a V7 Go Knowledge Hub of past research, allowing the agent to find correlations not just within the current project, but across your firm's entire history.

Cited and Explainable Insights

This is not a black box. Every identified correlation or pattern is supported by visual citations that link directly to the specific pieces of evidence in the source documents.

Cited and Explainable Insights

This is not a black box. Every identified correlation or pattern is supported by visual citations that link directly to the specific pieces of evidence in the source documents.

Cited and Explainable Insights

This is not a black box. Every identified correlation or pattern is supported by visual citations that link directly to the specific pieces of evidence in the source documents.

Cited and Explainable Insights

This is not a black box. Every identified correlation or pattern is supported by visual citations that link directly to the specific pieces of evidence in the source documents.

Reads across your entire dataset

To find the connections and contradictions that matter.

Get started

Get started

Import your files

SharePoint

,

Snowflake

,

Databricks

Import your files from whereever they are currently stored

Customer voices

Customer voices

Connect AI to your entire universe of data.

Connect AI to your entire universe of data.

Discover the insights you never knew to look for.

Discover the insights you never knew to look for.

Finance

Legal

Insurance

Tax

Real Estate

Finance

Legal

Insurance

Tax

Real Estate

Finance

Legal

Insurance

Tax

Real Estate

Customer Voices

Industrial equipment sales

We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.

Read the full story

Industrial equipment sales

We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.

Read the full story

Insurance

We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.

Read the full story

Insurance

We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.

Read the full story

Real Estate

Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.

Read the full story

Real Estate

Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.

Read the full story

Features

Features

Results you can actually trust.
Reliable AI document processing toolkit.

Results you can trust.
Trustworthy AI document processing toolkit.

Works with your entire data universe.

Structured and unstructured.

This agent is built to find insights across disparate data types, connecting a number in a database, a clause in a scanned PDF, a statement in an email, and a trend from a news feed into a single, cohesive insight.

Input types

Databases

PDFs

Unstructured Text

Multi-modal

Document types

Data Rooms

Shared Drives

Emails

Tables

Contracts

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Works with your entire data universe.

Structured and unstructured.

This agent is built to find insights across disparate data types, connecting a number in a database, a clause in a scanned PDF, a statement in an email, and a trend from a news feed into a single, cohesive insight.

Input types

Databases

PDFs

Unstructured Text

Multi-modal

Document types

Data Rooms

Shared Drives

Emails

Tables

Contracts

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Works with your entire data universe.

Structured and unstructured.

This agent is built to find insights across disparate data types, connecting a number in a database, a clause in a scanned PDF, a statement in an email, and a trend from a news feed into a single, cohesive insight.

Input types

Databases

PDFs

Unstructured Text

Multi-modal

Document types

Data Rooms

Shared Drives

Emails

Tables

Contracts

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Achieve near-perfect accuracy

with AI reasoning.

Meaningful insights depend on accurate connections. The agent uses AI reasoning to understand the context and semantics of your data, ensuring the patterns and contradictions it finds are logically sound, not just statistical noise.

Model providers

Security note

V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.

Answer

Type

Text

Tool

o4 Mini

Reasoning effort

Min

Low

Mid

High

AI Citations

Inputs

Set a prompt (Press @ to mention an input)

Achieve near-perfect accuracy

with AI reasoning.

Meaningful insights depend on accurate connections. The agent uses AI reasoning to understand the context and semantics of your data, ensuring the patterns and contradictions it finds are logically sound, not just statistical noise.

Model providers

Security note

V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.

Answer

Type

Text

Tool

o4 Mini

Reasoning effort

Min

Low

Mid

High

AI Citations

Inputs

Set a prompt (Press @ to mention an input)

Achieve near-perfect accuracy

with AI reasoning.

Meaningful insights depend on accurate connections. The agent uses AI reasoning to understand the context and semantics of your data, ensuring the patterns and contradictions it finds are logically sound, not just statistical noise.

Model providers

Security note

V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.

Answer

Type

Text

Tool

o4 Mini

Reasoning effort

Min

Low

Mid

High

AI Citations

Inputs

Set a prompt (Press @ to mention an input)

Trustworthy results,

grounded in your documents.

Never act on a black-box insight. Every correlation the agent identifies is supported by visual citations that link directly to the multiple pieces of source evidence across your documents, providing a fully transparent audit trail.

Visual grounding in action

00:54

Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.

Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.

Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.

Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.

Prior Warning and Ignoring Compliance

02

01

01

02

Trustworthy results,

grounded in your documents.

Never act on a black-box insight. Every correlation the agent identifies is supported by visual citations that link directly to the multiple pieces of source evidence across your documents, providing a fully transparent audit trail.

Visual grounding in action

00:54

Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.

Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.

Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.

Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.

Prior Warning and Ignoring Compliance

02

01

01

02

Trustworthy results,

grounded in your documents.

Never act on a black-box insight. Every correlation the agent identifies is supported by visual citations that link directly to the multiple pieces of source evidence across your documents, providing a fully transparent audit trail.

Visual grounding in action

00:54

Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.

Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.

Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.

Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.

Prior Warning and Ignoring Compliance

02

01

01

02

Enterprise-grade security

for sensitive financial data.

Your combined business data is your most strategic and sensitive asset. The V7 Go platform is architected to protect it, processing everything within your secure, private environment.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive financial data.

Your combined business data is your most strategic and sensitive asset. The V7 Go platform is architected to protect it, processing everything within your secure, private environment.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive financial data.

Your combined business data is your most strategic and sensitive asset. The V7 Go platform is architected to protect it, processing everything within your secure, private environment.

Certifications

GPDR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

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Answers

Answers

What you need to know about our

AI Multi-Document Correlation Agent

How does it 'correlate' information from different documents?

It uses a combination of entity recognition and semantic understanding. It first identifies key entities (like company names, people, or products) and then analyzes the context around those entities in every document to find relationships and contradictions.

+

How does it 'correlate' information from different documents?

It uses a combination of entity recognition and semantic understanding. It first identifies key entities (like company names, people, or products) and then analyzes the context around those entities in every document to find relationships and contradictions.

+

How does it 'correlate' information from different documents?

It uses a combination of entity recognition and semantic understanding. It first identifies key entities (like company names, people, or products) and then analyzes the context around those entities in every document to find relationships and contradictions.

+

Can it work on a mix of structured and unstructured data?

Yes, this is its primary strength. It is designed to find the connections between a number in a spreadsheet, a clause in a legal contract, and a statement in an unstructured email thread to form a single, cohesive insight.

+

Can it work on a mix of structured and unstructured data?

Yes, this is its primary strength. It is designed to find the connections between a number in a spreadsheet, a clause in a legal contract, and a statement in an unstructured email thread to form a single, cohesive insight.

+

Can it work on a mix of structured and unstructured data?

Yes, this is its primary strength. It is designed to find the connections between a number in a spreadsheet, a clause in a legal contract, and a statement in an unstructured email thread to form a single, cohesive insight.

+

Do we need to tell it what to look for?

You can do both. You can give it a specific query (e.g., 'Compare revenue in document A vs. document B'), or you can use it for unsupervised discovery, asking it to find 'any interesting patterns or contradictions' in a dataset.

+

Do we need to tell it what to look for?

You can do both. You can give it a specific query (e.g., 'Compare revenue in document A vs. document B'), or you can use it for unsupervised discovery, asking it to find 'any interesting patterns or contradictions' in a dataset.

+

Do we need to tell it what to look for?

You can do both. You can give it a specific query (e.g., 'Compare revenue in document A vs. document B'), or you can use it for unsupervised discovery, asking it to find 'any interesting patterns or contradictions' in a dataset.

+

What kind of datasets is this best suited for?

It is most powerful on large, complex, and heterogeneous datasets where human analysis is impractical. Prime examples include M&A data rooms, e-discovery productions, scientific research libraries, and historical archives.

+

What kind of datasets is this best suited for?

It is most powerful on large, complex, and heterogeneous datasets where human analysis is impractical. Prime examples include M&A data rooms, e-discovery productions, scientific research libraries, and historical archives.

+

What kind of datasets is this best suited for?

It is most powerful on large, complex, and heterogeneous datasets where human analysis is impractical. Prime examples include M&A data rooms, e-discovery productions, scientific research libraries, and historical archives.

+

What does the final output look like?

The agent delivers a dynamic report that outlines the key findings, patterns, and contradictions. Each finding is presented as a clear statement, supported by the specific, cited evidence from the source documents.

+

What does the final output look like?

The agent delivers a dynamic report that outlines the key findings, patterns, and contradictions. Each finding is presented as a clear statement, supported by the specific, cited evidence from the source documents.

+

What does the final output look like?

The agent delivers a dynamic report that outlines the key findings, patterns, and contradictions. Each finding is presented as a clear statement, supported by the specific, cited evidence from the source documents.

+

How do we know the correlations are meaningful and not just random?

The agent presents the data and the correlation; the human analyst provides the final judgment. By providing full transparency and citations for every finding, the agent empowers your experts to quickly validate whether a correlation is a meaningful insight or just noise.

+

How do we know the correlations are meaningful and not just random?

The agent presents the data and the correlation; the human analyst provides the final judgment. By providing full transparency and citations for every finding, the agent empowers your experts to quickly validate whether a correlation is a meaningful insight or just noise.

+

How do we know the correlations are meaningful and not just random?

The agent presents the data and the correlation; the human analyst provides the final judgment. By providing full transparency and citations for every finding, the agent empowers your experts to quickly validate whether a correlation is a meaningful insight or just noise.

+

Next steps

Next steps

Ready to find the hidden alpha in your data?

Bring us your most complex dataset—a data room, a research archive, an e-discovery production. We'll show you the powerful, non-obvious insights our correlation agent can uncover.

Uncover hidden liabilities

in

supplier contracts.

V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:

  • Mercedes-Benz logo
    SMC  logo
    Centerline logo
    Alaris logo

Uncover hidden liabilities

in

supplier contracts.

V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:

  • Mercedes-Benz logo
    SMC  logo
    Centerline logo
    Alaris logo