Retrieval, agents, model serving and the platform underneath — built by someone who has run all of it at scale and can show you the numbers.
Book a consultation callSeven things, all aimed at the same problem: AI that works in a demo and has to keep working in production.
Hybrid retrieval across dense and lexical lanes, evaluation harnesses, and the golden sets that turn "it works" from an opinion into a number.
12M+ documents on OpenSearch Serverless · 40M vectors on Qdrant · p95 under 25ms at 1.5K QPS
Multi-step agents with tool-calling contracts, MCP integrations to internal APIs, human approval gates on anything irreversible, and audit trails on every decision path.
Bedrock Agents and Step Functions, in production
Event-driven orchestration across the systems you already run — scheduled pipelines, deployment automation, and data movement between internal tools, with the expensive steps gated so they only fire when something actually changed.
Release time 4 hours → 20 minutes · $120K/year in tooling costs removed
Model gateways, routing, streaming and rate limiting. Then the build-versus-buy call made with real numbers instead of preference.
55% lower per-token cost on batch · $180K of annual spend removed
Terraform for the whole footprint, EKS, CI/CD, GitOps delivery, observability, and compliance-ready architecture on AWS or Azure.
SOC 2 aligned · all model traffic on PrivateLink
APIs and connectors between internal tools, data platforms and the systems your business depends on. Rust, Go, Python, Java.
40K+ concurrent connections at sub-millisecond response
Model lifecycle with versioning, staged rollout, drift detection, automated evaluation gates in CI and one-command rollback.
Prototype to production: six weeks → five days
A broken retrieval stage throws no exception. It returns plausible, scored, confident results — and every stage downstream reports success. The ticket says done. The demo works.
I published a case where pure vector search returned a book's own back-of-book index as a top-three hit at 0.353 similarity, while the chapter that actually answered the question didn't place at all. Nothing failed. Nothing came back empty. The answer was simply wrong.
That is what an AI deliverable looks like when it isn't done — and it's why every system I build ships with a way to measure whether it's working.
Start with a call. Thirty minutes, no charge — you describe what's breaking, I tell you whether I can help and what it would take. If an audit is the right next step, it's five days at a fixed price.
Book a consultation callLast updated 13 September 2026