90% faster entity extraction
Named Entity Recognition Agent
Extract entities in seconds, not hours
Delegate entity extraction to a specialized AI agent. It reads through documents, identifies and categorizes named entities—people, organizations, locations, dates, and more—and delivers a clean, structured dataset ready for analysis or integration.

Ideal for
Data Scientists
Research Teams
Compliance Analysts

See Named Entity Recognition Agent in action
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See Named Entity Recognition Agent in action
Play video

See Named Entity Recognition Agent in action
Play video

Entity Extraction from Research
See Named Entity Recognition Agent in action
Play video
Time comparison
Time comparison
Traditional way
8-12 hours per dataset
With V7 Go agents
15-30 minutes
Average time saved
90%
Why V7 Go
Why V7 Go
Multi-Category Entity Recognition
Identifies and categorizes entities across multiple types: people, organizations, locations, dates, monetary values, products, and custom entity classes tailored to your domain.
Multi-Category Entity Recognition
Identifies and categorizes entities across multiple types: people, organizations, locations, dates, monetary values, products, and custom entity classes tailored to your domain.
Multi-Category Entity Recognition
Identifies and categorizes entities across multiple types: people, organizations, locations, dates, monetary values, products, and custom entity classes tailored to your domain.
Multi-Category Entity Recognition
Identifies and categorizes entities across multiple types: people, organizations, locations, dates, monetary values, products, and custom entity classes tailored to your domain.
Contextual Understanding
Goes beyond simple pattern matching. The agent understands context and disambiguates entities, correctly identifying when the same name refers to different entities or when variations refer to the same entity.
Contextual Understanding
Goes beyond simple pattern matching. The agent understands context and disambiguates entities, correctly identifying when the same name refers to different entities or when variations refer to the same entity.
Contextual Understanding
Goes beyond simple pattern matching. The agent understands context and disambiguates entities, correctly identifying when the same name refers to different entities or when variations refer to the same entity.
Contextual Understanding
Goes beyond simple pattern matching. The agent understands context and disambiguates entities, correctly identifying when the same name refers to different entities or when variations refer to the same entity.
Relationship Mapping
Extracts not just entities but also the relationships between them, creating a knowledge graph that reveals connections and dependencies within your documents.
Relationship Mapping
Extracts not just entities but also the relationships between them, creating a knowledge graph that reveals connections and dependencies within your documents.
Relationship Mapping
Extracts not just entities but also the relationships between them, creating a knowledge graph that reveals connections and dependencies within your documents.
Relationship Mapping
Extracts not just entities but also the relationships between them, creating a knowledge graph that reveals connections and dependencies within your documents.
Bulk Processing Capability
Processes hundreds of documents simultaneously, extracting entities from entire datasets in minutes rather than weeks of manual effort.
Bulk Processing Capability
Processes hundreds of documents simultaneously, extracting entities from entire datasets in minutes rather than weeks of manual effort.
Bulk Processing Capability
Processes hundreds of documents simultaneously, extracting entities from entire datasets in minutes rather than weeks of manual effort.
Bulk Processing Capability
Processes hundreds of documents simultaneously, extracting entities from entire datasets in minutes rather than weeks of manual effort.
Structured Output Format
Delivers results in clean, structured formats (JSON, CSV, Excel) that integrate seamlessly with your data pipelines, analytics tools, and knowledge management systems.
Structured Output Format
Delivers results in clean, structured formats (JSON, CSV, Excel) that integrate seamlessly with your data pipelines, analytics tools, and knowledge management systems.
Structured Output Format
Delivers results in clean, structured formats (JSON, CSV, Excel) that integrate seamlessly with your data pipelines, analytics tools, and knowledge management systems.
Structured Output Format
Delivers results in clean, structured formats (JSON, CSV, Excel) that integrate seamlessly with your data pipelines, analytics tools, and knowledge management systems.
Confidence Scoring
Each extracted entity includes a confidence score, allowing you to prioritize high-confidence extractions and flag uncertain results for human review.
Confidence Scoring
Each extracted entity includes a confidence score, allowing you to prioritize high-confidence extractions and flag uncertain results for human review.
Confidence Scoring
Each extracted entity includes a confidence score, allowing you to prioritize high-confidence extractions and flag uncertain results for human review.
Confidence Scoring
Each extracted entity includes a confidence score, allowing you to prioritize high-confidence extractions and flag uncertain results for human review.
Extracts entities from any text source
To build structured knowledge from unstructured data.
Get started
Get started
Import your files
Google Sheets
,
MongoDB
,
Salesforce
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 data pipeline.
Connect AI to your data pipeline.
Turn unstructured text into structured intelligence.
Turn unstructured text into structured intelligence.
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Customer Voices
Industrial equipment sales
Read the full story
Industrial equipment sales
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
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.
Supporting complex documents.
Up to 200 pages.
Documents come in all formats and conditions. This agent handles PDFs, scanned images, handwritten notes, web content, and mixed-format documents. It extracts entities regardless of layout, quality, or language.
Input types
50+ languages
Handwritten text
200 pages
Multi-modal
Document types
PDFs
Web content
Scanned images
Transcripts
Email text
Vendor_US.xlsx

3
Supply_2023.pptx

Review_Legal.pdf

Supporting complex documents.
Up to 200 pages.
Documents come in all formats and conditions. This agent handles PDFs, scanned images, handwritten notes, web content, and mixed-format documents. It extracts entities regardless of layout, quality, or language.
Input types
50+ languages
Handwritten text
200 pages
Multi-modal
Document types
PDFs
Web content
Scanned images
Transcripts
Email text
Vendor_US.xlsx

3
Supply_2023.pptx

Review_Legal.pdf

Supporting complex documents.
Up to 200 pages.
Documents come in all formats and conditions. This agent handles PDFs, scanned images, handwritten notes, web content, and mixed-format documents. It extracts entities regardless of layout, quality, or language.
Input types
50+ languages
Handwritten text
200 pages
Multi-modal
Document types
PDFs
Web content
Scanned images
Transcripts
Email text
Vendor_US.xlsx

3
Supply_2023.pptx

Review_Legal.pdf

Reach 99% accuracy rate
through GenAI reasoning.
Entity extraction requires precision. This agent uses multi-step validation and contextual reasoning to ensure high accuracy, with confidence scores for every extracted entity so you know what to trust.
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)
Reach 99% accuracy rate
through GenAI reasoning.
Entity extraction requires precision. This agent uses multi-step validation and contextual reasoning to ensure high accuracy, with confidence scores for every extracted entity so you know what to trust.
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)
Reach 99% accuracy rate
through GenAI reasoning.
Entity extraction requires precision. This agent uses multi-step validation and contextual reasoning to ensure high accuracy, with confidence scores for every extracted entity so you know what to trust.
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 reality.
Every extracted entity is linked back to its precise location in the source document. This complete audit trail makes results verifiable and allows you to quickly validate or correct extractions.

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 reality.
Every extracted entity is linked back to its precise location in the source document. This complete audit trail makes results verifiable and allows you to quickly validate or correct extractions.

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 reality.
Every extracted entity is linked back to its precise location in the source document. This complete audit trail makes results verifiable and allows you to quickly validate or correct extractions.

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 high-stake industries.
Your documents contain sensitive information. V7 Go processes all data within your secure environment, ensuring confidentiality and compliance with data protection standards. Your documents are never used for external model training.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for high-stake industries.
Your documents contain sensitive information. V7 Go processes all data within your secure environment, ensuring confidentiality and compliance with data protection standards. Your documents are never used for external model training.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for high-stake industries.
Your documents contain sensitive information. V7 Go processes all data within your secure environment, ensuring confidentiality and compliance with data protection standards. Your documents are never used for external model training.
Certifications
GPDR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
More agents
More agents
Explore more agents to help you
Explore more agents to help you
accelerate your data processing and analytics
More agents
Answers
Answers
What you need to know about our
Named Entity Recognition Agent
What types of entities can the agent recognize?
The agent recognizes standard entity types including people, organizations, locations, dates, monetary amounts, and products. You can also define custom entity types specific to your industry or use case, and the agent will learn to identify them across your documents.
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What types of entities can the agent recognize?
The agent recognizes standard entity types including people, organizations, locations, dates, monetary amounts, and products. You can also define custom entity types specific to your industry or use case, and the agent will learn to identify them across your documents.
+
What types of entities can the agent recognize?
The agent recognizes standard entity types including people, organizations, locations, dates, monetary amounts, and products. You can also define custom entity types specific to your industry or use case, and the agent will learn to identify them across your documents.
+
How does it handle entity disambiguation?
The agent uses contextual analysis to distinguish between entities with the same or similar names. It understands that 'Apple' in a technology context differs from 'Apple' in an agricultural context, and correctly categorizes each based on surrounding information.
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How does it handle entity disambiguation?
The agent uses contextual analysis to distinguish between entities with the same or similar names. It understands that 'Apple' in a technology context differs from 'Apple' in an agricultural context, and correctly categorizes each based on surrounding information.
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How does it handle entity disambiguation?
The agent uses contextual analysis to distinguish between entities with the same or similar names. It understands that 'Apple' in a technology context differs from 'Apple' in an agricultural context, and correctly categorizes each based on surrounding information.
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Can it work with multiple languages?
Yes. The agent supports entity recognition across 50+ languages, making it suitable for multinational organizations and multilingual document collections.
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Can it work with multiple languages?
Yes. The agent supports entity recognition across 50+ languages, making it suitable for multinational organizations and multilingual document collections.
+
Can it work with multiple languages?
Yes. The agent supports entity recognition across 50+ languages, making it suitable for multinational organizations and multilingual document collections.
+
What about handwritten or scanned documents?
The agent handles scanned documents and handwritten text through advanced OCR integration. It can extract entities from low-quality scans, historical documents, and mixed-format inputs.
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What about handwritten or scanned documents?
The agent handles scanned documents and handwritten text through advanced OCR integration. It can extract entities from low-quality scans, historical documents, and mixed-format inputs.
+
What about handwritten or scanned documents?
The agent handles scanned documents and handwritten text through advanced OCR integration. It can extract entities from low-quality scans, historical documents, and mixed-format inputs.
+
How accurate is the entity extraction?
The agent achieves high accuracy through multi-step validation and contextual reasoning. Each extracted entity is linked to its source location in the document, allowing for quick verification and correction if needed.
+
How accurate is the entity extraction?
The agent achieves high accuracy through multi-step validation and contextual reasoning. Each extracted entity is linked to its source location in the document, allowing for quick verification and correction if needed.
+
How accurate is the entity extraction?
The agent achieves high accuracy through multi-step validation and contextual reasoning. Each extracted entity is linked to its source location in the document, allowing for quick verification and correction if needed.
+
Can I customize entity categories for my domain?
Absolutely. You can define custom entity types and train the agent on examples from your domain. The agent learns your specific classification scheme and applies it consistently across all documents.
+
Can I customize entity categories for my domain?
Absolutely. You can define custom entity types and train the agent on examples from your domain. The agent learns your specific classification scheme and applies it consistently across all documents.
+
Can I customize entity categories for my domain?
Absolutely. You can define custom entity types and train the agent on examples from your domain. The agent learns your specific classification scheme and applies it consistently across all documents.
+
Next steps
Next steps
Still manually tagging entities in your documents?
Send us a sample document, and we'll show you how our agent can extract and categorize entities in minutes.
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: