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AI Production Readiness Audit
Review an LLM app or agent for eval coverage, observability, cost, reliability, security, safety, and operational risk.
Deliverable: findings report, risk register, and prioritized roadmap.
Cognifity helps teams audit, instrument, and harden AI systems before quality, cost, reliability, or safety issues reach customers. The work is practical: architecture review, eval strategy, observability, risk reduction, and a prioritized plan your team can execute.
LLM systems fail in ways traditional monitoring does not catch: silent model behavior changes, brittle prompts, weak evals, runaway token spend, retrieval mistakes, unsafe tool use, and agent workflows that look fine until a real user hits the edge case.
Advisory work gives your team a focused outside review from someone building production AI observability infrastructure every day. It also helps Cognifity stay grounded in real production problems while Verdict evolves.
Start with a bounded audit, then decide whether you need implementation help, a deeper architecture review, or fractional leadership.
Review an LLM app or agent for eval coverage, observability, cost, reliability, security, safety, and operational risk.
Instrument traces, define rubrics, create label sets, calibrate judges, and establish practical quality and cost monitoring workflows.
Assess model routing, RAG, tool use, agent orchestration, guardrails, failure modes, escalation paths, and deployment architecture.
Find token waste, latency bottlenecks, retries, oversized prompts, unstable output formats, and places where agent behavior can run away.
Design human-labeling, rubric, regression test, judge-alignment, and drift-monitoring workflows that match your actual workloads.
Senior technical judgment for teams that need product-minded AI architecture and execution help without a full-time hire.
The output should help your team make concrete decisions: what to fix now, what to measure next, what not to overbuild, and where the real production risk sits.
Current-state review of architecture, prompts, evals, observability, and production workflows.
Risk-ranked findings across quality, safety, cost, reliability, data exposure, and operational readiness.
Practical implementation plan with quick wins, deeper fixes, and clear tradeoffs.
Optional Verdict instrumentation and calibration plan when LLM behavior drift is part of the problem.
Send a short note about what you are building, what is already live, and what feels risky. We can scope the right first engagement from there.