Every new LLM in your production within 2 hours.
Cost and quality, compounding.
EvalMate turns a few representative examples into measurable quality criteria and a domain-trained reward model. The Divyam Router's experimentation layer uses that reward model to benchmark candidates across 100+ LLMs and train a selector; the selector routes each prompt to the best-fit model while the reward model scores a sample of production traffic off the critical path, recalibrating continuously as models evolve, traffic shifts, and economics change, so every model earns its traffic and you are never locked into one provider.
50%
Inference cost cut
First cycle. ~75% by year 1.
5%
Quality gain
Against your own quality bar.
2 hr
New-model adoption
From release to your production.
The power of compounding
Each cycle, evals refine routing. Routing produces better traces. Traces refine the next round of evals. Quality and cost move in your favor, indefinitely.
Two products. One closed loop.
Model Router
Intelligent inferencing layer
- Per-prompt routing across 100+ LLMs (OpenAI, Anthropic, Google, Meta, open weights)
- Same OpenAI SDK. One-line drop-in. Zero downtime on model switches.
- Auto-adopts new model releases in 2 hours via shadow testing
- Live leaderboard ranked by quality, cost, latency
- Real-time analytics, regression alerts, auto-rollback
EvalMate
Eval co-pilot
- Domain-trained Rewards Model from ~100 examples, 92% human agreement
- Run evals at a fraction of LLM-as-Judge cost
- Auto-evolves rubric and prompts; detects drift and coverage gaps
- Versioned criteria with full audit trail and traceability
- Standalone product, or paired with Router for the closed loop
