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
See AI Data Annotation Agent in action
Play video
See AI Data Annotation Agent in action
Play video
See AI Data Annotation Agent in action
Play video
Product Image Classification
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.
Annotates any data type with precision
To accelerate your machine learning pipeline.
Get started
Get started
Import your files
AWS
,
Google Cloud
,
Hugging Face
Import your files from whereever they are currently stored
All types of Business documents supported
Once imported our system extracts and organises the essentials
Built for the teams that handle high-stakes work
Built to scale workflows for teams that need speed and accuracy.
For Teams
For Teams
ML Engineering Teams
ML Engineering Teams
ML Engineering Teams
Stop waiting for labeled data. Get annotated datasets ready for training in hours instead of weeks, so your engineers can focus on model architecture and optimization.
Stop waiting for labeled data. Get annotated datasets ready for training in hours instead of weeks, so your engineers can focus on model architecture and optimization.
Stop waiting for labeled data. Get annotated datasets ready for training in hours instead of weeks, so your engineers can focus on model architecture and optimization.
Data Science & Research Teams
Data Science & Research Teams
Data Science & Research Teams
Accelerate experimentation and iteration. Quickly prepare multiple annotated datasets to test different hypotheses and validate model approaches faster.
Accelerate experimentation and iteration. Quickly prepare multiple annotated datasets to test different hypotheses and validate model approaches faster.
Accelerate experimentation and iteration. Quickly prepare multiple annotated datasets to test different hypotheses and validate model approaches faster.
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
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.
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
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 198 15" xmlns="http://www.w3.org/2000/svg"><defs ><clipPath id="J0QEw1Ctk-2037569988-stroke"><use href="%23J0QEw1Ctk-2037569988"/></clipPath><path d="M 0 1 C 0 0.448 0.448 0 1 0 L 197 0 C 197.552 0 198 0.448 198 1 L 198 9 C 198 9.552 197.552 10 197 10 L 76 10 L 76 14 C 76 14.552 75.552 15 75 15 L 1 15 C 0.448 15 0 14.552 0 14 Z" id="J0QEw1Ctk-2037569988"/></defs><use fill="rgba(124, 58, 237, 0.1)" height="15px" href="%23J0QEw1Ctk-2037569988" id="J0QEw1Ctk" width="198px"/><use clip-path="url(%23J0QEw1Ctk-2037569988-stroke)" fill="transparent" href="%23J0QEw1Ctk-2037569988" stroke-dasharray="0" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="4" stroke-width="1" stroke="rgb(124, 58, 237)"/></svg>)
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:
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board: