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

See AI LLM Evaluation Agent in action
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See AI LLM Evaluation Agent in action
Play video

See AI LLM Evaluation Agent in action
Play video

Model Output Evaluation
See AI LLM Evaluation Agent in action
Play video
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.
Get started
Get started
Import your files
OpenAI
,
Google Sheets
,
Snowflake
Import your files from whereever they are currently stored
All types of Business documents supported
Once imported our system extracts and organises the essentials
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
Industrial equipment sales
Read the full story
Industrial equipment sales
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
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 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
accelerate your AI and machine learning workflows
More agents
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:
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board: