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Tools, playbooks, and reference implementations from our AI cost & reliability work.

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rag-knowledge-base-chatbot

RAG Support AI Assistant - Enterprise chatbot with hybrid retrieval (BM25 + vector)

Python521 months ago

production-performance-audit

Production performance audit playbook + tools (P95/P99, DB bottlenecks, caching, load tests)

Python10MIT6 months ago
Production AI · LLM / RAG Optimization

AI Cost & Reliability
Engineering

Fix wrong answers and hallucinations, reduce inference cost, prevent regressions — then prove it with before / after benchmarks.

Audit-first delivery
Baseline before code
PRs shipped, not decks
Real engineering output
Benchmarks included
Before / after metrics
Start with an AI AuditView pricing
Availability

Response within 24 hours · NDA-friendly intake · Fixed-scope pricing

LLM cost too high?RAG wrong answers?LLM observability?
OptyxStack
OptyxStack
AI Performance Engineering

Engineering standard for production AI systems. Audit-first delivery with measurable outcomes under real load.

Services

  • AI Production AuditBaseline & failure analysis
  • Optimization Sprint (4–6 weeks)Cost, quality & reliability
  • Reliability RetainerMonthly governance · regression gates

Common problems

  • LLM cost too high?
  • RAG wrong answers?
  • High P95 latency?
  • LLM observability?

Resources

  • GitHub
  • Insights
  • LLM Audit
  • RAG Reliability
  • Latency & Serving
  • Artifacts & Controls
  • Method

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Response within 24 h · NDA-friendly intake