90% faster evaluation

AI LLM Evaluation Agent

Assess model quality at scale

Delegate LLM output evaluation to a specialized AI agent. It assesses model responses against your custom criteria, identifies quality issues, flags inconsistencies, and provides structured feedback. Free your team from manual review so they can focus on model improvement and deployment.

Ideal for

AI & ML Teams

Quality Assurance

Product Engineering

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See AI LLM Evaluation Agent in action

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See AI LLM Evaluation Agent in action

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See AI LLM Evaluation Agent in action

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Model Output Evaluation

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See AI LLM Evaluation Agent in action

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Time comparison

Time comparison

Traditional way

40-60 hours per batch

With V7 Go agents

15-30 minutes

Average time saved

90%

Why V7 Go

Why V7 Go

Custom Evaluation Criteria

Define your own quality metrics and evaluation standards. The agent assesses every output against your specific criteria, whether measuring accuracy, tone, completeness, or domain-specific requirements.

Custom Evaluation Criteria

Define your own quality metrics and evaluation standards. The agent assesses every output against your specific criteria, whether measuring accuracy, tone, completeness, or domain-specific requirements.

Custom Evaluation Criteria

Define your own quality metrics and evaluation standards. The agent assesses every output against your specific criteria, whether measuring accuracy, tone, completeness, or domain-specific requirements.

Custom Evaluation Criteria

Define your own quality metrics and evaluation standards. The agent assesses every output against your specific criteria, whether measuring accuracy, tone, completeness, or domain-specific requirements.

Consistency Detection

Identifies inconsistencies across model outputs, flagging responses that deviate from established patterns or violate your quality standards, ensuring uniform performance.

Consistency Detection

Identifies inconsistencies across model outputs, flagging responses that deviate from established patterns or violate your quality standards, ensuring uniform performance.

Consistency Detection

Identifies inconsistencies across model outputs, flagging responses that deviate from established patterns or violate your quality standards, ensuring uniform performance.

Consistency Detection

Identifies inconsistencies across model outputs, flagging responses that deviate from established patterns or violate your quality standards, ensuring uniform performance.

Bias & Fairness Analysis

Detects potential biases, fairness issues, or problematic patterns in model outputs, helping you identify and address systemic quality problems before deployment.

Bias & Fairness Analysis

Detects potential biases, fairness issues, or problematic patterns in model outputs, helping you identify and address systemic quality problems before deployment.

Bias & Fairness Analysis

Detects potential biases, fairness issues, or problematic patterns in model outputs, helping you identify and address systemic quality problems before deployment.

Bias & Fairness Analysis

Detects potential biases, fairness issues, or problematic patterns in model outputs, helping you identify and address systemic quality problems before deployment.

Batch Processing at Scale

Evaluates hundreds or thousands of model outputs simultaneously, completing in minutes what would take your team weeks of manual review and assessment.

Batch Processing at Scale

Evaluates hundreds or thousands of model outputs simultaneously, completing in minutes what would take your team weeks of manual review and assessment.

Batch Processing at Scale

Evaluates hundreds or thousands of model outputs simultaneously, completing in minutes what would take your team weeks of manual review and assessment.

Batch Processing at Scale

Evaluates hundreds or thousands of model outputs simultaneously, completing in minutes what would take your team weeks of manual review and assessment.

Structured Feedback Generation

Produces detailed, actionable feedback for each output, including specific improvement suggestions and severity ratings to guide model refinement and retraining.

Structured Feedback Generation

Produces detailed, actionable feedback for each output, including specific improvement suggestions and severity ratings to guide model refinement and retraining.

Structured Feedback Generation

Produces detailed, actionable feedback for each output, including specific improvement suggestions and severity ratings to guide model refinement and retraining.

Structured Feedback Generation

Produces detailed, actionable feedback for each output, including specific improvement suggestions and severity ratings to guide model refinement and retraining.

Auditable Evaluation Trail

Creates a complete record of every evaluation decision with full reasoning and citations. Every assessment is transparent and defensible for compliance and quality assurance purposes.

Auditable Evaluation Trail

Creates a complete record of every evaluation decision with full reasoning and citations. Every assessment is transparent and defensible for compliance and quality assurance purposes.

Auditable Evaluation Trail

Creates a complete record of every evaluation decision with full reasoning and citations. Every assessment is transparent and defensible for compliance and quality assurance purposes.

Auditable Evaluation Trail

Creates a complete record of every evaluation decision with full reasoning and citations. Every assessment is transparent and defensible for compliance and quality assurance purposes.

Evaluates model outputs comprehensively

To deliver objective quality assessments.

Customer voices

Customer voices

Measure model quality objectively.

Measure model quality objectively.

Scale evaluation without scaling your team.

Scale evaluation without scaling your team.

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 diverse output formats.

From any source.

Model outputs come in many forms. This agent evaluates responses from any LLM, in any format—text, JSON, structured data, or unstructured content. It adapts to your evaluation needs, not the other way around.

Input types

Multiple LLMs

Custom Criteria

Batch Processing

Multi-format

Document types

JSON

CSV

Spreadsheets

Text

Logs

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Supporting diverse output formats.

From any source.

Model outputs come in many forms. This agent evaluates responses from any LLM, in any format—text, JSON, structured data, or unstructured content. It adapts to your evaluation needs, not the other way around.

Input types

Multiple LLMs

Custom Criteria

Batch Processing

Multi-format

Document types

JSON

CSV

Spreadsheets

Text

Logs

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Supporting diverse output formats.

From any source.

Model outputs come in many forms. This agent evaluates responses from any LLM, in any format—text, JSON, structured data, or unstructured content. It adapts to your evaluation needs, not the other way around.

Input types

Multiple LLMs

Custom Criteria

Batch Processing

Multi-format

Document types

JSON

CSV

Spreadsheets

Text

Logs

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Consistent evaluation standards

applied uniformly.

Eliminate subjective variation in evaluation. The agent applies your criteria consistently across every output, ensuring fair, objective assessment. No more inconsistent reviews or missed quality issues.

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 evaluation standards

applied uniformly.

Eliminate subjective variation in evaluation. The agent applies your criteria consistently across every output, ensuring fair, objective assessment. No more inconsistent reviews or missed quality issues.

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 evaluation standards

applied uniformly.

Eliminate subjective variation in evaluation. The agent applies your criteria consistently across every output, ensuring fair, objective assessment. No more inconsistent reviews or missed quality issues.

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 assessments,

fully explained.

Every evaluation decision is transparent and auditable. The agent provides detailed reasoning for each assessment, showing exactly why an output passed or failed your criteria. No black boxes, just clear accountability.

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 assessments,

fully explained.

Every evaluation decision is transparent and auditable. The agent provides detailed reasoning for each assessment, showing exactly why an output passed or failed your criteria. No black boxes, just clear accountability.

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 assessments,

fully explained.

Every evaluation decision is transparent and auditable. The agent provides detailed reasoning for each assessment, showing exactly why an output passed or failed your criteria. No black boxes, just clear accountability.

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 AI work.

Model outputs and evaluation criteria are your proprietary assets. V7 Go processes all evaluation data within your secure environment. Your models, outputs, and quality standards are never shared or used for external purposes.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive AI work.

Model outputs and evaluation criteria are your proprietary assets. V7 Go processes all evaluation data within your secure environment. Your models, outputs, and quality standards are never shared or used for external purposes.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive AI work.

Model outputs and evaluation criteria are your proprietary assets. V7 Go processes all evaluation data within your secure environment. Your models, outputs, and quality standards are never shared or used for external purposes.

Certifications

GPDR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

More agents

More agents

Explore more agents to help you

Explore more agents to help you

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Answers

Answers

What you need to know about our

AI LLM Evaluation Agent

How do we define evaluation criteria?

You define your evaluation criteria through a simple configuration process. Specify what constitutes quality for your use case—accuracy metrics, tone requirements, completeness checks, or domain-specific standards. The agent then applies these criteria consistently across all outputs.

+

How do we define evaluation criteria?

You define your evaluation criteria through a simple configuration process. Specify what constitutes quality for your use case—accuracy metrics, tone requirements, completeness checks, or domain-specific standards. The agent then applies these criteria consistently across all outputs.

+

How do we define evaluation criteria?

You define your evaluation criteria through a simple configuration process. Specify what constitutes quality for your use case—accuracy metrics, tone requirements, completeness checks, or domain-specific standards. The agent then applies these criteria consistently across all outputs.

+

Can it evaluate outputs from any LLM?

Yes. The agent is model-agnostic and can evaluate outputs from any language model, whether from OpenAI, Anthropic, Google, open-source models, or your own fine-tuned versions. It assesses the quality of the output, not the source.

+

Can it evaluate outputs from any LLM?

Yes. The agent is model-agnostic and can evaluate outputs from any language model, whether from OpenAI, Anthropic, Google, open-source models, or your own fine-tuned versions. It assesses the quality of the output, not the source.

+

Can it evaluate outputs from any LLM?

Yes. The agent is model-agnostic and can evaluate outputs from any language model, whether from OpenAI, Anthropic, Google, open-source models, or your own fine-tuned versions. It assesses the quality of the output, not the source.

+

How does it handle subjective quality dimensions?

The agent uses multi-step reasoning to assess subjective dimensions like tone, clarity, and appropriateness. It applies your defined standards consistently and flags borderline cases for human review, combining automation with expert judgment.

+

How does it handle subjective quality dimensions?

The agent uses multi-step reasoning to assess subjective dimensions like tone, clarity, and appropriateness. It applies your defined standards consistently and flags borderline cases for human review, combining automation with expert judgment.

+

How does it handle subjective quality dimensions?

The agent uses multi-step reasoning to assess subjective dimensions like tone, clarity, and appropriateness. It applies your defined standards consistently and flags borderline cases for human review, combining automation with expert judgment.

+

What format should model outputs be in?

The agent accepts outputs in any format—plain text, JSON, CSV, Excel, or structured documents. It can process batches of outputs from logs, databases, or files, making integration with your existing evaluation pipelines straightforward.

+

What format should model outputs be in?

The agent accepts outputs in any format—plain text, JSON, CSV, Excel, or structured documents. It can process batches of outputs from logs, databases, or files, making integration with your existing evaluation pipelines straightforward.

+

What format should model outputs be in?

The agent accepts outputs in any format—plain text, JSON, CSV, Excel, or structured documents. It can process batches of outputs from logs, databases, or files, making integration with your existing evaluation pipelines straightforward.

+

How do we use evaluation results to improve models?

The agent delivers structured feedback that identifies patterns in failures and quality issues. This data feeds directly into model retraining, prompt optimization, and deployment decisions, creating a continuous improvement loop.

+

How do we use evaluation results to improve models?

The agent delivers structured feedback that identifies patterns in failures and quality issues. This data feeds directly into model retraining, prompt optimization, and deployment decisions, creating a continuous improvement loop.

+

How do we use evaluation results to improve models?

The agent delivers structured feedback that identifies patterns in failures and quality issues. This data feeds directly into model retraining, prompt optimization, and deployment decisions, creating a continuous improvement loop.

+

Is evaluation data kept confidential?

Absolutely. V7 Go processes all evaluation data within your secure environment. Model outputs and evaluation criteria remain your proprietary assets and are never used for external purposes or model training.

+

Is evaluation data kept confidential?

Absolutely. V7 Go processes all evaluation data within your secure environment. Model outputs and evaluation criteria remain your proprietary assets and are never used for external purposes or model training.

+

Is evaluation data kept confidential?

Absolutely. V7 Go processes all evaluation data within your secure environment. Model outputs and evaluation criteria remain your proprietary assets and are never used for external purposes or model training.

+

Next steps

Next steps

Spending too much time evaluating model outputs?

Send us a sample batch of model outputs and your evaluation criteria. We'll show you how to automate assessment and free your team for higher-value work.

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