The Problem
You're Deploying Agents You Haven't Properly Evaluated.
Most teams evaluate AI on a handful of examples, eyeball the results, and ship. In production, agents reason across multi-turn conversations, invoke tools, and make autonomous decisions — evaluation needs to match that complexity.
Infrastructure Burden
Setting up evaluation pipelines, managing compute, parallelising test runs — teams spend more time on infrastructure than on actually evaluating their agents.
Not Reproducible
Different team members get different results from the same evaluation. No versioning, no audit trail, no way to compare runs reliably.
Single-Turn Only
Traditional benchmarks test single Q&A pairs. Real agents reason across multi-turn trajectories — your evaluation should too.
No Indic Coverage
Global benchmarks don't test Indian languages meaningfully. Your agents serve users in Hindi, Tamil, Telugu, Bengali — your evaluation must cover them.
Capabilities
Evaluation Without the Infrastructure
Submit evaluation jobs via API. Eval handles compute, parallelisation, and aggregation. You focus on what matters — whether your agents are good enough for production.
Trajectory Evaluation
Evaluate multi-turn agent conversations and reasoning chains end-to-end. Test the full trajectory, not just individual responses.
Response Evaluation
Custom criteria with user-defined scoring functions. Test factual accuracy, helpfulness, safety, and domain-specific quality metrics.
Model Comparison
Side-by-side dashboards for quality, latency, and cost tradeoffs across models. Make informed decisions about which model serves your use case.
CI/CD Integration
GitHub Actions, GitLab CI, webhooks. Run evaluations on every commit, every PR, every deployment. Quality gates for your agent pipeline.
Indic Language Benchmarks
Built on Rota Labs' Indic Eval framework. Evaluate agent performance across 22 Indian languages with culturally-calibrated test sets.
Custom Evaluators
Build domain-specific evaluators for your industry. Medical accuracy for healthcare agents. Regulatory compliance for financial agents. Safety checks for any deployment.
How It Works
Four Steps to Rigorous Evaluation
Define
Write evaluation specs — what to test, what criteria to use, what passing looks like
Submit
Send jobs via API or CI/CD pipeline. No infrastructure to manage, no compute to provision
Execute
Serverless parallel execution. Eval handles scaling, orchestration, and aggregation
Review
Results in dashboards and exportable reports. Compare runs, track regressions, enforce quality gates
Manual Evaluation
- Eyeball a few examples and ship
- Results vary by who runs them
- No versioning or audit trail
- Single-turn tests only
- English benchmarks for Indian users
Eval
- Systematic evaluation at scale
- 100% reproducible with versioned specs
- Full history and comparison across runs
- Multi-turn trajectory evaluation
- 22 Indic languages with cultural calibration
Open Source Foundation
Built on rotalabs-eval
Eval is built on Rota Labs' open-source LLM evaluation framework. The managed platform adds serverless infrastructure, team collaboration, CI/CD integration, and enterprise features — but the core evaluation engine is open and auditable.
India Deployment
Data Localisation: All evaluation data processed within Indian boundaries.
Indic Coverage: 22 languages with culturally-calibrated benchmarks.
Compliance: DPDP Act ready. No evaluation data leaves sovereign infrastructure.
If you can't evaluate it rigorously,
you shouldn't deploy it.
