Practical guides, architecture notes, and lessons from shipping GenAI to production.
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.
An AI automation consultant finds the high-ROI work worth automating with AI and builds it. Where it pays off, what to automate first, AI vs RPA, and how to choose.
What an AI integration consultant does for software companies: where AI belongs in your product, wiring it into your stack, and shipping features that hold up in production.
An AI consultant helps your business find where AI actually fits, then helps build it. What they do, what they don't, the types, the cost, and when to hire one.
A practical guide to designing production RAG systems against explicit cost and latency budgets without sacrificing quality, covering retrieval, reranking, routing, caching, async orchestration, and evals.