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

See AI Multi-Document Correlation Agent in action
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See AI Multi-Document Correlation Agent in action
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See AI Multi-Document Correlation Agent in action
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Diligence Consistency Check
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
All types of Business documents supported
Once imported our system extracts and organises the essentials
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
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
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
Finance
“Whenever I think about hiring, I first try to do it in V7 Go.” Discover how HITICCO uses V7 Go agents to accelerate and enrich their prospect research.
Read the full story
Finance
The experience with V7 has been fantastic. Very customized level of support. You feel like they really care about your outcome and objectives.
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:
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board: