A research-grade process for production systems
We treat product delivery like engineering, not improvisation. That means clear problem framing, measurable quality, disciplined implementation, and operational readiness from day one. Especially for AI, where “it works on my laptop” is not a strategy.
01
Frame
Define the objective, constraints, and success metrics. Align stakeholders on scope, risk, and what “good” looks like in production.
02
Architect
Choose the right system design: data model, interfaces, security boundaries, and deployment topology. Build for maintainability, not heroics.
03
Implement
Production-grade development with code review, test strategy, and predictable releases. Clean logs, typed contracts, and clear ownership.
04
Validate
Measure quality with evaluation. For AI: retrieval accuracy, hallucination checks, safety guardrails, and regression testing against real scenarios.
05
Deploy
CI/CD, secrets, observability, and rollback paths. We ship with confidence and avoid fragile one-off deployments.
06
Operate
Monitoring, incident readiness, performance tuning, and maintenance. Systems stay fast, stable, and secure long after launch.
DeliveryMilestones, written acceptance criteria, and weekly progress signals
EngineeringCode review, testing, typed interfaces, and maintainable architecture
AI rigorBenchmarks, eval harnesses, retrieval tuning, and regression controls
SecurityLeast privilege, secrets management, audit trails, and safe defaults
OpsObservability, alerts, backups, and rollback paths that actually work