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
Time comparison
Traditional way
40-60 hours per batch
With V7 Go agents
15-30 minutes
Average time saved
90%
Why V7 Go
Evaluates model outputs comprehensively
To deliver objective quality assessments.


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
Measure model quality objectively.
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
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.
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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.
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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.
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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.
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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.
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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.
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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.












