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

  • 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 RLHF Feedback 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 RLHF Feedback 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 RLHF Feedback Agent in action

Play video

Model Feedback Processing

  • 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 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

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

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

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:

  • 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