AI Evaluation & Reliability Testing
Rigorous benchmarking, hallucination tracking, and latency profiling for enterprise AI systems.
Operational Problems Solved
Don't deploy AI blindly. We implement automated evaluation suites (Ragas, DeepEval) that measure response fidelity, toxicity, latency, and cost before code merges.
System Architecture & Execution Flow
DETERMINISTIC PIPELINEGolden Dataset
Curates verified input-output pairs representing typical and adversarial user requests.
Automated Suite
Runs model outputs against evaluation metrics on every Git PR.
Score & Gate
Blocks deployment if Faithfulness score drops below 95% threshold.
Live Telemetry
Monitors production conversations for drift and semantic degradation.
What We Deliver
Human-in-the-Loop Review Points
- Subject matter experts periodically review and expand the golden benchmark dataset
Data Privacy & Security Boundaries
- Synthetic data generation used for test cases to avoid exposing real user data in test environments
Technical Questions
Why do we need automated AI testing?
Prompt engineering without regression testing is risky: fixing a prompt for one use case often breaks three others. Automated evaluation gives you quantitative confidence before deploying changes to live customers.
Scope a Production Pilot
We typically deliver a functional staging proof-of-concept for this capability within 2–3 weeks.
Recommended Tech Stack
Ready to scope your AI Evaluation & Reliability Testing?
Share your current tech stack and dataset requirements. We will prepare an architecture proposal within one business day.
Zero obligation • Direct technical conversation with engineers • NDA upon request