AI Evaluation
We benchmark quality, accuracy, and reliability before anything reaches production. Rigorous LLM, agent, and RAG evaluation — paired with hallucination detection — gives you confidence in what you ship.
Book a ConsultationEverything under AI Evaluation
LLM evaluation
Systematic benchmarking of model outputs against task-specific quality, accuracy, and safety criteria.
Agent evaluation
End-to-end testing of agent trajectories, tool use, and task completion — not just final output quality.
RAG evaluation
Retrieval and generation quality metrics — precision, recall, faithfulness, and answer relevance.
Hallucination detection
Automated checks that flag ungrounded or fabricated claims before they reach production.
Quality & reliability benchmarks
Repeatable benchmark suites and regression tracking so quality improves — and never silently degrades — release over release.
Where AI Evaluation fits in the end-to-end lifecycle.
- 1
Architecture
Design resilient, scalable agentic system architecture.
- 2
Build
Engineer agents, workflows, and orchestration logic.
- 3
RAG
Ground agents in enterprise knowledge with retrieval pipelines.
- 4
MCP
Connect agents to enterprise tools and systems via MCP.
- 5
Evaluate
Benchmark quality, accuracy, and reliability pre-launch.
- 6
Secure
Harden systems against prompt, data, and access risks.
- 7
Deploy
Ship to production with CI/CD and cloud-native infra.
- 8
Observe
Trace, monitor, and audit agent behavior in real time.
- 9
Optimize
Tune cost, latency, and performance continuously.
- 10
Operate
Run day-to-day operations with autonomous, self-healing systems.
Explore other pillars
Ready to build production-grade agentic AI?
Talk to our team about architecture, evaluation, and production operations.
Book a Consultation