We don't start with models. We start with the workflow.
Over the past 18 months, we have deployed 60+ custom AI integrations. Through trial and error, we have standardized a build process designed to guarantee delivery without breaking client operations.
Phase 1: Shadowing and Discovery
We embed an architect alongside your team. We don't read your SOP documents — we watch your team do the work. We record screen shares, trace where files get stuck, and identify the actual database schemas involved. The output is a complete operational map.
Phase 2: Prompt Engineering and Feasibility PoCs
We test prompt variables against real data samples. We measure accuracy, latency, and token costs. If a model fails to extract data with 98%+ precision, we redesign the prompt chain or declare the task infeasible. We de-risk the math before writing integration code.
Phase 3: Core Pipeline Build
We write the orchestrator, set up row-level security parameters, and connect the API routes. We build the operator panels so your team can monitor agent logs and override decisions when needed.
Phase 4: Staged Sandbox Launches
We deploy to isolated staging databases, running historical data through the system to audit accuracy. We transition to live operations only after clearing the acceptance parameters.