Services
AI Guardrails & Evals Review
Review the controls, tests, monitoring, and fallback paths that keep LLMs, RAG systems, copilots, and agents safe in production.
Technical review for AI products that need reliable behavior under real product conditions. Covers policy boundaries, refusal behavior, retrieval constraints, eval design, regression tests, output monitoring, abuse detection, escalation paths, and fallback handling.
Best for
AI Product Lead, Product Security, Trust and Safety, Engineering Lead
Engagement model
implementation
Duration
3-6 weeks
Deliverables
4 deliverables
What it covers
Guardrail architecture and refusal/fallback review
Eval set and abuse case design
Regression testing strategy
Monitoring, telemetry, and QA workflow recommendations
Use when
Related people
David Wolf
Builds operating models, controls, detection, and evidence layers for enterprise AI adoption.
Alex Eisen
Leads vulnerability research, incident response, product security, and AI risk management work.
James Traynor
Builds defensive controls, AI-first training, and practical vendor-aware workflows.
Related proof
Start here
Scope this review through discovery, then translate the result into engineering work, buyer-ready evidence, or a follow-on engagement.
Canonical route: /services/ai-guardrails-evals-review