From hospital management system to clinical AI: an end-to-end case study
How we build a hospital management platform end-to-end — ADT, EHR on FHIR, orders, pharmacy, scheduling, beds and revenue cycle — then layer an LLM stack on top that cuts documentation time, speeds discharges and lowers claim denials. The architecture, the one rule that governs it, and the metrics.
Inside a real-time AI fraud & risk platform: an end-to-end architecture
A full end-to-end architecture for a real-time payment fraud-and-risk platform: event ingestion, an online feature store, a sub-100ms rules+ML hot path, and an LLM layer built ground-up — kept off the hot path — plus human-in-the-loop case management and the governance plane regulators demand.
Multi-tenant AI: isolating data, cost, and noisy neighbors
One AI platform serving many businesses has three isolation problems — data, cost, and performance. The patterns we use: tenant_id as a first-class key end to end, per-tenant metering and budgets, noisy-neighbor defenses, and per-tenant evals and observability.
Idea to production in weeks: how a small senior team ships AI
"Weeks not quarters" isn't heroics — it's scope discipline. One thin vertical slice, evals as the spec, and a production-readiness checklist a technical buyer should actually demand.
How we build AI agents that hold up in production
A production architecture for LLM agents: the control loop, tool design, guardrails, retries and fallbacks, evals, and observability — what separates a demo from something you can trust.
RAG that actually retrieves: patterns we use in production
Most RAG failures are retrieval failures, not model failures. The chunking, hybrid search, reranking, query rewriting, grounding and evaluation patterns that make RAG reliable.