Approach
A path from an ambiguous ask to a system someone else can run.
The same sequence shows up in defense platforms, biomedical pipelines, and high-traffic services. The names change. The gates do not.
01
Discovery
We sit with product, QA, platform, and ML science when they own a piece, and turn the ask into engineering tasks.
02
Architecture
Service boundaries, API contracts, and data flow are written down before the code multiplies.
03
Tests and evaluation gates
pytest, regression suites, and, where the product is a model, an evaluation that can fail the change.
04
Observability
Prometheus, Grafana, and Loki, aimed at latency, failures, and the questions an on-call engineer asks.
05
Handoff and mentoring
Documentation, code review, and mentoring so the team that owns the system can operate it.
Trustworthy AI, as we have shipped it
Explainability, guardrails, and an audit trail. We use those words for layers we have put on production agents, not as a poster.
Agentic software delivery
We use AI coding assistants to move faster on implementation, tests, and documentation. Review and the evaluation gates stay in the path. The assistant does not sign the change.
Ways to engage
Five shapes. No rate card. Scope is agreed in writing before a build starts.
Architecture review
A written architecture and a sequenced task list from a goal that is still fuzzy.
Build and deliver
We implement the services, the tests, and the delivery path.
Platform hardening
Evaluation gates, continuous delivery, and observability on a system you already run.
AI-readiness assessment
A review of data access, evaluation, and the deployment path before a model feature is funded.
Team enablement
Code review, design sessions, and mentoring on the stack we actually use.