The Formula: Domain Expertise + Engineering Rigor + AI

ERP integrations, order management, purchasing workflows, inventory reconciliation, vendor data feeds. We have built inside these systems. We know where the business logic lives, where the edge cases hide, and which requirements only surface after the first integration test.

In healthcare and pharma, that means GxP validation, 21 CFR Part 11, data integrity requirements, electronic batch records. In manufacturing and supply chain, it means bill-of-materials complexity, MES integration, warehouse orchestration, supplier data quality.

We arrive with that knowledge. It changes the timeline and the quality of every architecture decision from day one.

AI can generate code fast. Without discipline, that speed creates problems faster than it solves them. We have built the process that governs that speed in complex enterprise environments: codified architecture standards, structured implementation plans, near-complete automated test coverage, and CI/CD pipelines that validate every output before it ships.

We wrote about the mechanics in detail in Why AI Coding Fails. The short version: every line of generated code follows codified standards, runs against a thorough test suite, and passes human review before it ships.

Claude Code, Anthropic's agentic engineering tool, is our current execution layer. It reads the full codebase, plans changes across multiple files, generates and runs tests continuously. It can iterate through failures autonomously. It produces near-complete test coverage and full CI/CD pipelines.

This is the speed multiplier that works because of the domain expertise and engineering rigor above it. Without those, AI generates plausible code that breaks in production. With them, it compresses timelines by an order of magnitude while maintaining the quality bar.