90% faster annotation

AI Data Annotation Agent

Label datasets in hours, not weeks

Delegate dataset annotation to a specialized AI agent. It classifies images, extracts entities, labels text, and creates structured training data with consistency and speed. Your ML team can focus on model architecture and optimization instead of manual labeling.

Ideal for

ML Engineering Teams

Data Science

AI Research

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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 Data Annotation 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 Data Annotation 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 Data Annotation Agent in action

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Product Image Classification

  • 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 Data Annotation Agent in action

Play video

Time comparison

Time comparison

Traditional way

4-8 weeks

With V7 Go agents

4-8 hours

Average time saved

90%

Why V7 Go

Why V7 Go

Multi-Modal Classification

Handles images, text, audio, and video with consistent labeling logic. The agent understands context and applies domain-specific classification rules across all data types.

Multi-Modal Classification

Handles images, text, audio, and video with consistent labeling logic. The agent understands context and applies domain-specific classification rules across all data types.

Multi-Modal Classification

Handles images, text, audio, and video with consistent labeling logic. The agent understands context and applies domain-specific classification rules across all data types.

Multi-Modal Classification

Handles images, text, audio, and video with consistent labeling logic. The agent understands context and applies domain-specific classification rules across all data types.

Entity Extraction & Tagging

Automatically identifies and tags named entities, objects, and attributes within unstructured data. Produces structured annotations ready for model training.

Entity Extraction & Tagging

Automatically identifies and tags named entities, objects, and attributes within unstructured data. Produces structured annotations ready for model training.

Entity Extraction & Tagging

Automatically identifies and tags named entities, objects, and attributes within unstructured data. Produces structured annotations ready for model training.

Entity Extraction & Tagging

Automatically identifies and tags named entities, objects, and attributes within unstructured data. Produces structured annotations ready for model training.

Confidence Scoring & Quality Control

Assigns confidence scores to each annotation and flags uncertain cases for human review. Ensures high-quality training data without sacrificing speed.

Confidence Scoring & Quality Control

Assigns confidence scores to each annotation and flags uncertain cases for human review. Ensures high-quality training data without sacrificing speed.

Confidence Scoring & Quality Control

Assigns confidence scores to each annotation and flags uncertain cases for human review. Ensures high-quality training data without sacrificing speed.

Confidence Scoring & Quality Control

Assigns confidence scores to each annotation and flags uncertain cases for human review. Ensures high-quality training data without sacrificing speed.

Consistency Across Massive Datasets

Applies identical labeling rules to millions of data points, eliminating the inconsistency that plagues crowdsourced annotation. Every label follows the same logic.

Consistency Across Massive Datasets

Applies identical labeling rules to millions of data points, eliminating the inconsistency that plagues crowdsourced annotation. Every label follows the same logic.

Consistency Across Massive Datasets

Applies identical labeling rules to millions of data points, eliminating the inconsistency that plagues crowdsourced annotation. Every label follows the same logic.

Consistency Across Massive Datasets

Applies identical labeling rules to millions of data points, eliminating the inconsistency that plagues crowdsourced annotation. Every label follows the same logic.

Custom Taxonomy Support

Define your own classification schemes and labeling rules. The agent learns your taxonomy and applies it consistently across your entire dataset.

Custom Taxonomy Support

Define your own classification schemes and labeling rules. The agent learns your taxonomy and applies it consistently across your entire dataset.

Custom Taxonomy Support

Define your own classification schemes and labeling rules. The agent learns your taxonomy and applies it consistently across your entire dataset.

Custom Taxonomy Support

Define your own classification schemes and labeling rules. The agent learns your taxonomy and applies it consistently across your entire dataset.

Export-Ready Formats

Delivers annotations in standard formats (COCO, Pascal VOC, YOLO, JSON) compatible with popular ML frameworks. No conversion or reformatting needed.

Export-Ready Formats

Delivers annotations in standard formats (COCO, Pascal VOC, YOLO, JSON) compatible with popular ML frameworks. No conversion or reformatting needed.

Export-Ready Formats

Delivers annotations in standard formats (COCO, Pascal VOC, YOLO, JSON) compatible with popular ML frameworks. No conversion or reformatting needed.

Export-Ready Formats

Delivers annotations in standard formats (COCO, Pascal VOC, YOLO, JSON) compatible with popular ML frameworks. No conversion or reformatting needed.

Customer voices

Customer voices

Accelerate model development.

Accelerate model development.

Get labeled data ready in hours, not weeks.

Get labeled data ready in hours, not weeks.

Finance

Legal

Insurance

Tax

Real Estate

Finance

Legal

Insurance

Tax

Real Estate

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 any data type.

At any scale.

Whether you're annotating product images, customer reviews, medical scans, or sensor data, this agent adapts to your data type and taxonomy. It scales from thousands to millions of data points without losing consistency.

Input types

Images

Text

Audio

Video

Document types

JPG/PNG

CSV/JSON

MP3/WAV

MP4/MOV

Cloud Storage

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Supporting any data type.

At any scale.

Whether you're annotating product images, customer reviews, medical scans, or sensor data, this agent adapts to your data type and taxonomy. It scales from thousands to millions of data points without losing consistency.

Input types

Images

Text

Audio

Video

Document types

JPG/PNG

CSV/JSON

MP3/WAV

MP4/MOV

Cloud Storage

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Supporting any data type.

At any scale.

Whether you're annotating product images, customer reviews, medical scans, or sensor data, this agent adapts to your data type and taxonomy. It scales from thousands to millions of data points without losing consistency.

Input types

Images

Text

Audio

Video

Document types

JPG/PNG

CSV/JSON

MP3/WAV

MP4/MOV

Cloud Storage

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Consistent labeling

across millions of data points.

Unlike crowdsourced annotation, this agent applies identical logic to every data point. No human inconsistency, no label drift, no quality degradation as datasets grow. Your training data stays clean and consistent.

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)

Consistent labeling

across millions of data points.

Unlike crowdsourced annotation, this agent applies identical logic to every data point. No human inconsistency, no label drift, no quality degradation as datasets grow. Your training data stays clean and consistent.

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)

Consistent labeling

across millions of data points.

Unlike crowdsourced annotation, this agent applies identical logic to every data point. No human inconsistency, no label drift, no quality degradation as datasets grow. Your training data stays clean and consistent.

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)

Confidence-driven quality.

Human review where it matters.

Every annotation includes a confidence score. Low-confidence items are automatically flagged for human review, ensuring your training data quality while still achieving massive time savings. You get the best of both worlds.

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

Confidence-driven quality.

Human review where it matters.

Every annotation includes a confidence score. Low-confidence items are automatically flagged for human review, ensuring your training data quality while still achieving massive time savings. You get the best of both worlds.

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

Confidence-driven quality.

Human review where it matters.

Every annotation includes a confidence score. Low-confidence items are automatically flagged for human review, ensuring your training data quality while still achieving massive time savings. You get the best of both worlds.

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 datasets.

Your training data is proprietary and sensitive. V7 Go processes all annotations within your secure environment. Datasets are never shared, never used for external purposes, and never exposed to third parties.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive datasets.

Your training data is proprietary and sensitive. V7 Go processes all annotations within your secure environment. Datasets are never shared, never used for external purposes, and never exposed to third parties.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive datasets.

Your training data is proprietary and sensitive. V7 Go processes all annotations within your secure environment. Datasets are never shared, never used for external purposes, and never exposed to third parties.

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 Data Annotation Agent

How does the agent handle ambiguous or edge-case data?

The agent assigns confidence scores to each annotation. Low-confidence items are automatically flagged for human review, ensuring your training data quality remains high while still achieving massive time savings.

+

How does the agent handle ambiguous or edge-case data?

The agent assigns confidence scores to each annotation. Low-confidence items are automatically flagged for human review, ensuring your training data quality remains high while still achieving massive time savings.

+

How does the agent handle ambiguous or edge-case data?

The agent assigns confidence scores to each annotation. Low-confidence items are automatically flagged for human review, ensuring your training data quality remains high while still achieving massive time savings.

+

Can it learn from my existing labeled data?

Yes. You can provide a small set of labeled examples, and the agent uses them as reference patterns to apply consistent logic to your unlabeled dataset. This ensures annotations match your specific requirements.

+

Can it learn from my existing labeled data?

Yes. You can provide a small set of labeled examples, and the agent uses them as reference patterns to apply consistent logic to your unlabeled dataset. This ensures annotations match your specific requirements.

+

Can it learn from my existing labeled data?

Yes. You can provide a small set of labeled examples, and the agent uses them as reference patterns to apply consistent logic to your unlabeled dataset. This ensures annotations match your specific requirements.

+

What data formats does it support?

The agent handles images (JPG, PNG, TIFF), text (TXT, CSV, JSON), audio (WAV, MP3), and video (MP4, MOV). It can also process data directly from cloud storage or databases.

+

What data formats does it support?

The agent handles images (JPG, PNG, TIFF), text (TXT, CSV, JSON), audio (WAV, MP3), and video (MP4, MOV). It can also process data directly from cloud storage or databases.

+

What data formats does it support?

The agent handles images (JPG, PNG, TIFF), text (TXT, CSV, JSON), audio (WAV, MP3), and video (MP4, MOV). It can also process data directly from cloud storage or databases.

+

How do I define custom labels and categories?

You provide a taxonomy document or examples of your labeling scheme. The agent learns your classification logic and applies it consistently. You can refine rules iteratively based on results.

+

How do I define custom labels and categories?

You provide a taxonomy document or examples of your labeling scheme. The agent learns your classification logic and applies it consistently. You can refine rules iteratively based on results.

+

How do I define custom labels and categories?

You provide a taxonomy document or examples of your labeling scheme. The agent learns your classification logic and applies it consistently. You can refine rules iteratively based on results.

+

Is the annotated data secure and private?

Absolutely. V7 Go processes your data within your secure environment. Annotations are never shared or used for external purposes. Your datasets remain completely private.

+

Is the annotated data secure and private?

Absolutely. V7 Go processes your data within your secure environment. Annotations are never shared or used for external purposes. Your datasets remain completely private.

+

Is the annotated data secure and private?

Absolutely. V7 Go processes your data within your secure environment. Annotations are never shared or used for external purposes. Your datasets remain completely private.

+

What's the typical turnaround time for large datasets?

The agent can annotate thousands of data points per hour, depending on complexity. A dataset of 100,000 images typically takes 2-4 hours. Exact timing depends on your taxonomy and data characteristics.

+

What's the typical turnaround time for large datasets?

The agent can annotate thousands of data points per hour, depending on complexity. A dataset of 100,000 images typically takes 2-4 hours. Exact timing depends on your taxonomy and data characteristics.

+

What's the typical turnaround time for large datasets?

The agent can annotate thousands of data points per hour, depending on complexity. A dataset of 100,000 images typically takes 2-4 hours. Exact timing depends on your taxonomy and data characteristics.

+

Next steps

Next steps

Still manually labeling your training data?

Send us a sample dataset and we'll show you how fast intelligent annotation can be done. See the difference in quality and speed.

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