80% faster feedback processing
AI RLHF Feedback Agent
Turn feedback into model improvement
Delegate the tedious work of collecting and structuring human feedback to a specialized AI agent. It processes annotations, identifies patterns in model errors, categorizes quality issues, and generates structured training datasets ready for model fine-tuning. Your team focuses on providing feedback; the agent handles the rest.
Ideal for
ML Engineering Teams
AI Product Teams
Data Science Teams
See AI RLHF Feedback Agent in action
Play video
See AI RLHF Feedback Agent in action
Play video
See AI RLHF Feedback Agent in action
Play video
Model Feedback Processing
See AI RLHF Feedback Agent in action
Play video
Time comparison
Time comparison
Traditional way
8-12 hours per batch
With V7 Go agents
30-45 minutes
Average time saved
85%
Why V7 Go
Why V7 Go
Automated Annotation Processing
Ingests human feedback from any format—spreadsheets, forms, comments, or structured databases—and normalizes it into a consistent, machine-readable structure.
Automated Annotation Processing
Ingests human feedback from any format—spreadsheets, forms, comments, or structured databases—and normalizes it into a consistent, machine-readable structure.
Automated Annotation Processing
Ingests human feedback from any format—spreadsheets, forms, comments, or structured databases—and normalizes it into a consistent, machine-readable structure.
Automated Annotation Processing
Ingests human feedback from any format—spreadsheets, forms, comments, or structured databases—and normalizes it into a consistent, machine-readable structure.
Error Pattern Recognition
Identifies recurring failure modes and clusters similar errors to reveal systemic weaknesses in model behavior that require targeted training data.
Error Pattern Recognition
Identifies recurring failure modes and clusters similar errors to reveal systemic weaknesses in model behavior that require targeted training data.
Error Pattern Recognition
Identifies recurring failure modes and clusters similar errors to reveal systemic weaknesses in model behavior that require targeted training data.
Error Pattern Recognition
Identifies recurring failure modes and clusters similar errors to reveal systemic weaknesses in model behavior that require targeted training data.
Quality Scoring & Prioritization
Assigns confidence scores to feedback, prioritizes high-impact corrections, and flags low-quality or contradictory annotations for human review.
Quality Scoring & Prioritization
Assigns confidence scores to feedback, prioritizes high-impact corrections, and flags low-quality or contradictory annotations for human review.
Quality Scoring & Prioritization
Assigns confidence scores to feedback, prioritizes high-impact corrections, and flags low-quality or contradictory annotations for human review.
Quality Scoring & Prioritization
Assigns confidence scores to feedback, prioritizes high-impact corrections, and flags low-quality or contradictory annotations for human review.
Training Dataset Generation
Transforms validated feedback into structured training examples ready for fine-tuning, with proper formatting for your chosen model framework.
Training Dataset Generation
Transforms validated feedback into structured training examples ready for fine-tuning, with proper formatting for your chosen model framework.
Training Dataset Generation
Transforms validated feedback into structured training examples ready for fine-tuning, with proper formatting for your chosen model framework.
Training Dataset Generation
Transforms validated feedback into structured training examples ready for fine-tuning, with proper formatting for your chosen model framework.
Feedback Loop Tracking
Maintains a complete audit trail of which feedback was incorporated into which model versions, enabling you to measure the impact of each training iteration.
Feedback Loop Tracking
Maintains a complete audit trail of which feedback was incorporated into which model versions, enabling you to measure the impact of each training iteration.
Feedback Loop Tracking
Maintains a complete audit trail of which feedback was incorporated into which model versions, enabling you to measure the impact of each training iteration.
Feedback Loop Tracking
Maintains a complete audit trail of which feedback was incorporated into which model versions, enabling you to measure the impact of each training iteration.
Continuous Improvement Workflows
Automates the entire cycle from feedback collection through model retraining, reducing the time between identifying errors and deploying improvements.
Continuous Improvement Workflows
Automates the entire cycle from feedback collection through model retraining, reducing the time between identifying errors and deploying improvements.
Continuous Improvement Workflows
Automates the entire cycle from feedback collection through model retraining, reducing the time between identifying errors and deploying improvements.
Continuous Improvement Workflows
Automates the entire cycle from feedback collection through model retraining, reducing the time between identifying errors and deploying improvements.
Transforms feedback into training data
To accelerate model improvement cycles.
Get started
Get started
Import your files
Google Sheets
,
Notion
,
GitHub
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 manually processing feedback annotations. Let the agent handle data normalization and quality control so your engineers can focus on model architecture and optimization.
Stop manually processing feedback annotations. Let the agent handle data normalization and quality control so your engineers can focus on model architecture and optimization.
Stop manually processing feedback annotations. Let the agent handle data normalization and quality control so your engineers can focus on model architecture and optimization.
AI Product & Operations Teams
AI Product & Operations Teams
AI Product & Operations Teams
Accelerate your feedback-to-improvement cycle. Systematize user feedback and error reports into actionable training signals that drive continuous model enhancement.
Accelerate your feedback-to-improvement cycle. Systematize user feedback and error reports into actionable training signals that drive continuous model enhancement.
Accelerate your feedback-to-improvement cycle. Systematize user feedback and error reports into actionable training signals that drive continuous model enhancement.
Customer voices
Customer voices
Connect feedback to model advancement.
Connect feedback to model advancement.
Trusted by leading AI teams.
Trusted by leading AI teams.
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 feedback format.
From any source.
Feedback comes from everywhere—spreadsheets, databases, annotation platforms, comment fields, and survey tools. This agent normalizes all formats into a unified structure, handling inconsistent schemas, missing fields, and varied data types seamlessly.
Input types
Spreadsheets
Databases
Unstructured Text
Multi-format
Document types
CSV
JSON
Excel
SQL
Text
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Supporting any feedback format.
From any source.
Feedback comes from everywhere—spreadsheets, databases, annotation platforms, comment fields, and survey tools. This agent normalizes all formats into a unified structure, handling inconsistent schemas, missing fields, and varied data types seamlessly.
Input types
Spreadsheets
Databases
Unstructured Text
Multi-format
Document types
CSV
JSON
Excel
SQL
Text
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Supporting any feedback format.
From any source.
Feedback comes from everywhere—spreadsheets, databases, annotation platforms, comment fields, and survey tools. This agent normalizes all formats into a unified structure, handling inconsistent schemas, missing fields, and varied data types seamlessly.
Input types
Spreadsheets
Databases
Unstructured Text
Multi-format
Document types
CSV
JSON
Excel
SQL
Text
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Reach 99% data consistency
through validation and deduplication.
Feedback quality directly impacts model improvement. This agent validates every annotation, detects duplicates and contradictions, and scores reliability to ensure your training data is clean and trustworthy.
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% data consistency
through validation and deduplication.
Feedback quality directly impacts model improvement. This agent validates every annotation, detects duplicates and contradictions, and scores reliability to ensure your training data is clean and trustworthy.
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% data consistency
through validation and deduplication.
Feedback quality directly impacts model improvement. This agent validates every annotation, detects duplicates and contradictions, and scores reliability to ensure your training data is clean and trustworthy.
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 feedback,
fully auditable.
Every piece of feedback is tracked from source through incorporation into training data. The agent maintains a complete audit trail showing which feedback influenced which model versions, enabling you to measure impact and troubleshoot regressions.
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
Trustworthy feedback,
fully auditable.
Every piece of feedback is tracked from source through incorporation into training data. The agent maintains a complete audit trail showing which feedback influenced which model versions, enabling you to measure impact and troubleshoot regressions.
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 feedback,
fully auditable.
Every piece of feedback is tracked from source through incorporation into training data. The agent maintains a complete audit trail showing which feedback influenced which model versions, enabling you to measure impact and troubleshoot regressions.
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 model data.
Feedback often contains proprietary information and user data. V7 Go processes all feedback within your secure environment with end-to-end encryption. Your training signals are never shared externally or used for third-party model development.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for sensitive model data.
Feedback often contains proprietary information and user data. V7 Go processes all feedback within your secure environment with end-to-end encryption. Your training signals are never shared externally or used for third-party model development.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for sensitive model data.
Feedback often contains proprietary information and user data. V7 Go processes all feedback within your secure environment with end-to-end encryption. Your training signals are never shared externally or used for third-party model development.
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 RLHF Feedback Agent
How does the agent handle conflicting feedback?
The agent flags contradictory annotations and surfaces them for human arbitration. It tracks the source and confidence level of each piece of feedback, allowing your team to make informed decisions about which signals to prioritize.
+
How does the agent handle conflicting feedback?
The agent flags contradictory annotations and surfaces them for human arbitration. It tracks the source and confidence level of each piece of feedback, allowing your team to make informed decisions about which signals to prioritize.
+
How does the agent handle conflicting feedback?
The agent flags contradictory annotations and surfaces them for human arbitration. It tracks the source and confidence level of each piece of feedback, allowing your team to make informed decisions about which signals to prioritize.
+
What feedback formats can it process?
The agent accepts feedback from spreadsheets, survey responses, annotation platforms, comment fields, and structured databases. It normalizes all formats into a unified structure for analysis and training data generation.
+
What feedback formats can it process?
The agent accepts feedback from spreadsheets, survey responses, annotation platforms, comment fields, and structured databases. It normalizes all formats into a unified structure for analysis and training data generation.
+
What feedback formats can it process?
The agent accepts feedback from spreadsheets, survey responses, annotation platforms, comment fields, and structured databases. It normalizes all formats into a unified structure for analysis and training data generation.
+
Can it integrate with our model training pipeline?
Yes. The agent outputs training datasets in standard formats (JSONL, CSV, Parquet) compatible with major ML frameworks. It can also integrate directly with your fine-tuning infrastructure via API.
+
Can it integrate with our model training pipeline?
Yes. The agent outputs training datasets in standard formats (JSONL, CSV, Parquet) compatible with major ML frameworks. It can also integrate directly with your fine-tuning infrastructure via API.
+
Can it integrate with our model training pipeline?
Yes. The agent outputs training datasets in standard formats (JSONL, CSV, Parquet) compatible with major ML frameworks. It can also integrate directly with your fine-tuning infrastructure via API.
+
How does it measure feedback quality?
The agent analyzes annotation consistency, checks for logical contradictions, validates against ground truth where available, and scores feedback based on annotator reliability and specificity.
+
How does it measure feedback quality?
The agent analyzes annotation consistency, checks for logical contradictions, validates against ground truth where available, and scores feedback based on annotator reliability and specificity.
+
How does it measure feedback quality?
The agent analyzes annotation consistency, checks for logical contradictions, validates against ground truth where available, and scores feedback based on annotator reliability and specificity.
+
Does it work with proprietary or custom models?
Absolutely. The agent is model-agnostic. It processes feedback for any AI system—whether it's a custom LLM, specialized classifier, or proprietary model—and generates training data in your preferred format.
+
Does it work with proprietary or custom models?
Absolutely. The agent is model-agnostic. It processes feedback for any AI system—whether it's a custom LLM, specialized classifier, or proprietary model—and generates training data in your preferred format.
+
Does it work with proprietary or custom models?
Absolutely. The agent is model-agnostic. It processes feedback for any AI system—whether it's a custom LLM, specialized classifier, or proprietary model—and generates training data in your preferred format.
+
How is feedback data secured?
V7 Go processes all feedback within your secure environment with enterprise-grade encryption. Feedback data is never shared externally or used for third-party model training. You maintain complete control over your training signals.
+
How is feedback data secured?
V7 Go processes all feedback within your secure environment with enterprise-grade encryption. Feedback data is never shared externally or used for third-party model training. You maintain complete control over your training signals.
+
How is feedback data secured?
V7 Go processes all feedback within your secure environment with enterprise-grade encryption. Feedback data is never shared externally or used for third-party model training. You maintain complete control over your training signals.
+
Next steps
Next steps
Still manually processing feedback annotations?
Send us a sample of your feedback data, and we'll show you how to turn it into structured training signals that improve your models.
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: