Practical guides, architecture notes, and lessons from shipping GenAI to production.
A production guide to confidence and abstention policies for GenAI systems: uncertainty signals, threshold tuning, evals, UX patterns, and observability to reduce false confidence.
A production-focused guide to reliable schema-bound LLM outputs: contract design, validation, repair, retries, evals, observability, and downstream safety.
A practical guide to prompt and response caching for LLM apps: semantic keys, invalidation, tenant isolation, freshness checks, safety guardrails, observability, and cost/latency tradeoffs.
A production guide to versioning the RAG knowledge layer with dual indexes, shadow rebuilds, retrieval diffing, rollback plans, and release gates to prevent silent quality or compliance regressions.
A production-focused guide to when GenAI systems should stop, ask, defer, or hand off to humans, with escalation policies, queue design, SLAs, auditability, and learning loops.
A production guide to monitoring and responding to drift in RAG systems: corpus changes, query shifts, retrieval regressions, answer-grounding decay, canary evals, alerting, and rollback.
A practical guide to tracing retrieval, prompts, tool calls, routing, and failures in production LLM systems so teams can debug regressions, connect quality to cost/latency, and improve faster.
A production-focused guide to building routing layers across fast, cheap, and premium models using cascades, escalation, specialization, evals, and fallback patterns that cut cost and latency without sacrificing quality or safety.
A production-focused guide to building ACL-aware enterprise RAG systems that prevent permission leakage from ingestion through retrieval, caching, reranking, and generation.
A production-focused guide to deciding when fine-tuning beats prompt, retrieval, or tool improvements—plus eval design, cost/latency tradeoffs, and operational guidance.
Skip the paid 'top AI consulting companies' lists. How to figure out which firm (if any) fits your situation, what to ask, and how to get real value from the spend.
AI governance consulting puts the guardrails, policy, and evals in place so cautious teams can adopt AI safely. What it covers, why it matters, and how to choose.