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

  • Mercedes-Benz logo
    SMC  logo
    Gradient rectangle
    Centerline logo
    Abstract graphic with black and white elements
    Abstract pattern with overlapping shapes.
    Alaris logo
    Progress bar with text at bottom percentage
    SITO logo
    Diagonal stripes on a rectangular background
    SVG logo for Reddit
    Grey and black abstract symbol
    Foobar logo
    ABL logo
    SVG logo for Google
    Bar chart with two bars of different heights
    Brotherhood Mutual logo
    Black and white abstract graphic.
    Paige logo
    Roche logo
    Logo with abstract shapes and text.
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Light gray waveform pattern on white background
    SVG logo with black letters
    Abstract pattern with blue and green colors

See Named Entity Recognition Agent in action

Play video

  • Mercedes-Benz logo
    SMC  logo
    Gradient rectangle
    Centerline logo
    Abstract graphic with black and white elements
    Abstract pattern with overlapping shapes.
    Alaris logo
    Progress bar with text at bottom percentage
    SITO logo
    Diagonal stripes on a rectangular background
    SVG logo for Reddit
    Grey and black abstract symbol
    Foobar logo
    ABL logo
    SVG logo for Google
    Bar chart with two bars of different heights
    Brotherhood Mutual logo
    Black and white abstract graphic.
    Paige logo
    Roche logo
    Logo with abstract shapes and text.
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Light gray waveform pattern on white background
    SVG logo with black letters
    Abstract pattern with blue and green colors

See Named Entity Recognition Agent in action

Play video

Entity Extraction from Research

  • Mercedes-Benz logo
    SMC  logo
    Gradient rectangle
    Centerline logo
    Abstract graphic with black and white elements
    Abstract pattern with overlapping shapes.
    Alaris logo
    Progress bar with text at bottom percentage
    SITO logo
    Diagonal stripes on a rectangular background
    SVG logo for Reddit
    Grey and black abstract symbol
    Foobar logo
    ABL logo
    SVG logo for Google
    Bar chart with two bars of different heights
    Brotherhood Mutual logo
    Black and white abstract graphic.
    Paige logo
    Roche logo
    Logo with abstract shapes and text.
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Light gray waveform pattern on white background
    SVG logo with black letters
    Abstract pattern with blue and green colors

See Named Entity Recognition Agent in action

Play video

Time comparison

Traditional way

8-12 hours per dataset

With V7 Go agents

15-30 minutes

Average time saved

90%

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.

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.

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.

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

Logo
Logo
Logo

Import your files

Google Sheets

,

MongoDB

,

Salesforce

Import your files from whereever they are currently stored

Customer voices

Customer voices

Connect AI to your data pipeline.

Turn unstructured text into structured intelligence.

Turn unstructured text into structured intelligence.

Finance

Legal

Insurance

Tax

Real Estate

Customer Voices

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

12

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

12

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

OpenAI, Anthropic, Gemini logos

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

OpenAI, Anthropic, Gemini logos

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

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

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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.

+

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.

+

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.

+

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.

+

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:

  • Logo with abstract shapes and text.
    SITO logo
    Abstract pattern with blue and green colors
    SVG logo for Google
    Progress bar with text at bottom percentage
    Bar chart with two bars of different heights
    Gradient rectangle
    Diagonal stripes on a rectangular background
    Abstract pattern with overlapping shapes.
    SVG logo for Reddit

Uncover hidden liabilities

in

supplier contracts.

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

  • Logo with abstract shapes and text.
    SITO logo
    Abstract pattern with blue and green colors
    SVG logo for Google
    Progress bar with text at bottom percentage
    Bar chart with two bars of different heights
    Gradient rectangle
    Diagonal stripes on a rectangular background
    Abstract pattern with overlapping shapes.
    SVG logo for Reddit