AI's Hardest Problem is Evaluation, Not Intelligence
EvalMate helps you define what "correct" means for your agents, then automates evaluation so you can ship with confidence.
Building AI agents is hard. Evaluating them is harder.
Define Correctness
Every agent needs its own definition of quality. Domain experts carry it in their heads but struggle to write it down. Different team members have different standards. Without a shared definition, you're guessing.
- Needs access to domain experts
- Hard to articulate what "good" looks like
- Divergent mental models across teams
Evaluate Correctness
Manual review doesn't scale past a few hundred examples. Generic LLM judges don't match your quality bar. Custom judges are expensive to build and maintain.
- Impossible to scale with human annotators
- Off-the-shelf LLM judges don't align with your team
- Aligned judges are costly to operate at scale
From 100 examples to automated evaluation at scale
Define your quality bar
Tell EvalMate what good looks like. Share a handful of examples (~100 preferences) and EvalMate's agentic workflow proposes, critiques, and refines a rubric that captures your team's definition of correctness.
Build an aligned judge
EvalMate fine-tunes an LLM judge that scores responses the way your team would. Using smart sampling, it needs only ~1,000 human-reviewed examples to match your annotators at 10x lower cost.
Scale evaluation automatically
EvalMate distills your judge into a compact reward model (~8B parameters) that runs on your infrastructure. Evaluate every response, continuously, at 100x lower cost than human review.
Your single source of truth for quality
The rubric is a structured checklist of evaluation dimensions, each with a weight and scale. It becomes the shared language between your team, your LLM judge, and your reward model. Everyone evaluates against the same criteria.
- Consistent evaluation across human reviewers, LLM judges, and automated models
- Evolves with your product as requirements change
- Captures nuance: not just pass/fail, but weighted dimensions like helpfulness, correctness, and coherence
Once you can evaluate reliably, everything else follows
Fine-tune on your own data
Use evaluation signals to fine-tune smaller models on your proprietary data, improving quality while reducing cost. Your reward model provides the training signal, no additional annotation needed.
Route prompts to the right model
Train a lightweight classifier that sends each request to the best model for the job. Simple queries go to fast, affordable models. Complex ones go to frontier models. Cut costs without losing quality.
Learn more about Divyam.AI RouterReal impact from production deployments
"EvalMate helped us define what quality means for our AI workflows and continuously measure it. The results spoke for themselves."
Common Questions About EvalMate
How many examples do I need to get started with EvalMate?
You only need about 100 human preferences to start. EvalMate's agentic workflow uses these to propose, critique, and refine a structured rubric that captures your team's definition of quality. From there, it uses smart sampling to build an aligned judge from roughly 1,000 annotations, and scales to a full reward model from about 10,000 judge annotations.
How accurate is EvalMate's automated judge compared to human reviewers?
EvalMate's LLM judge achieves approximately 92% agreement with human annotators. This is comparable to the typical inter-annotator agreement between two human reviewers. The judge is fine-tuned specifically on your team's quality standards, not generic evaluation criteria, which is why agreement rates are significantly higher than off-the-shelf LLM judges.
Can EvalMate run on my own infrastructure?
Yes. EvalMate's final stage distills your aligned judge into a compact reward model of approximately 8 billion parameters that runs entirely on your infrastructure. This means your evaluation data never leaves your environment, and you can evaluate every single response in production at 100x lower cost than human review.
How does EvalMate connect to model routing?
EvalMate's evaluation signals feed directly into Divyam.AI's Model Router. Once you have a reliable measure of quality, the router can train a lightweight classifier that sends each request to the best model for the job — simple queries to fast, affordable models and complex ones to frontier models. The evaluation pipeline also provides the training signal for fine-tuning smaller models on your proprietary data, with no additional annotation needed.
Start evaluating with confidence
Your agents deserve better than vibes-based testing. EvalMate gives your team a shared quality bar and automated evaluation pipeline that scales.