Building
The build phase is where plans become working systems. I develop iteratively, ship prototypes early, and test against real data — so you see tangible progress from the start, not just status updates.
How I develop
I don't disappear for weeks and come back with a finished product. Development happens in short cycles with regular check-ins, so you're always in the loop.
Iterative Development
I build in short cycles — typically weekly — with working demos at each milestone. This means you can give feedback on real, functioning systems rather than slides or mockups.
Working Prototypes Early
You'll see something running within the first few days, not the last. Early prototypes validate assumptions quickly and let us course-correct before too much is built on shaky foundations.
Regular Check-ins
Short, focused check-ins keep the project aligned with your expectations. I share progress, flag blockers, and confirm direction — no surprises at delivery time.
What gets built
Every project is different, but the build phase typically involves some combination of these core activities.
Agent Development
Building purpose-built AI agents with well-defined roles, system prompts, tool access, and decision logic. Each agent is designed for a specific job within your workflow.
Pipeline Construction
Data pipelines that ingest, transform, and route information reliably. Whether it's document processing, API integration, or real-time event handling — data flows are built for production, not just demos.
MCP Integrations
Custom Model Context Protocol servers that give your AI agents secure, structured access to internal systems — databases, APIs, file stores, CRMs, and more.
Integration with Existing Systems
AI systems don't exist in isolation. I integrate with your current tools, platforms, and workflows so the new systems enhance what you already have rather than replacing it.
Testing against reality
I don't test with contrived examples. Every system is validated against real data and real conditions before deployment. This means feeding actual documents through pipelines, triggering agents with real events, and running workflows end-to-end in environments that mirror production.
Edge cases, error handling, and failure modes get as much attention as the happy path. The goal is systems that work reliably when things go wrong — not just when everything is perfect.
The output of the build phase is a set of working systems, tested and ready for deployment. Not a prototype, not a proof of concept — production-ready systems with proper error handling, logging, and documentation.
Have a project in mind?
Let's talk about what you need built and how I can help.