01 / Discovery
Map stakeholders, integration seams on legacy stacks, data classifications, and KPIs so pilots cannot succeed only on vanity metrics.
Process
From Pilot to Production
Our delivery process assumes real-world constraints: messy APIs, legacy schemas, audit requirements, and users who will abandon AI features if quality regresses. MCP integrations inherit the same rigour as customer-facing AI products.
02 / System Design
Choose model routing, MCP tool contracts, retrieval boundaries, orchestration patterns, and explicit failure handling—including human overrides.
03 / Build and Integrate
Implement agents, retrieval pipelines, and frontend experiences with progressive enhancement.
04 / Evals and Hardening
Run quality evaluations, edge-case tests, and guardrail checks before production release.
05 / Launch and Observe
Monitor quality, latency, and cost with instrumentation and actionable reporting.
06 / Optimize
Continuously improve prompts, tool logic, retrieval quality, and model selection over time.