Every engagement runs on the same backbone: a contract grounded in a technical specification and business analysis, delivered in phases and closed with acceptance certificates. From there, the design path forks — one route for ML systems, another for everything else.
Before a line of code, we agree on exactly what success looks like. The contract is the single source of truth — scope, deliverables, timeline and how each milestone is signed off — so there are no surprises on either side.
We start from the business goal, not the feature list. I map stakeholders, processes, constraints and the metrics that define value — turning a vague brief into a clear problem statement.
The analysis becomes a written technical specification: functional and non-functional requirements, architecture, interfaces and acceptance criteria — the contract's backbone.
Work is broken into phases with defined outcomes and budgets. Each phase ships something real and reviewable, so progress is visible and risk stays low.
Every phase closes with a signed acceptance certificate against its criteria. Clear hand-offs, clear payments, a clean paper trail from kickoff to delivery.
I model the software around the business domain, not the database. Using a Domain-Driven Design (DDD) approach, the specification and the code speak the same language as the people who run the business — which keeps the system coherent as it grows and makes change cheap instead of dangerous.
Once requirements are clear, the design follows a deliberate framework rather than improvisation. Which framework depends on the nature of the problem.
For GenAI / ML problems, we move through a seven-step path — from clarifying the goal to running, monitored models in production.
For conventional software, we follow a system-design route: scope, estimate, model the contracts and architecture, then harden it for scale and reliability.