What we build

The services listed above are categories, not packages. Most of our real engagements cross multiple categories because most real problems do. A typical AI project involves some amount of backend development for data ingestion, a web interface for the humans who will use the output, observability and monitoring so the system does not silently degrade, and occasional geospatial work if the data is location-aware. Rather than forcing clients into a fixed tier, we scope each engagement around the specific outcome and bring in whatever mix of skills the outcome requires.

When you engage us for AI work, the first question we ask is what success looks like measurably. If the answer is faster document processing, we benchmark the current process and set a concrete target for the new system. If the answer is better search results inside an internal knowledge base, we build an evaluation harness that lets us compare versions quantitatively. We do this because AI systems are notoriously easy to demo and notoriously hard to keep working in production, and the difference between a demo and a production system is almost always the monitoring, evaluation, and fallback tooling that sits around the model itself. We build those together as a single engagement, not as an afterthought.

For web and platform engagements we write maintainable, boring code on purpose. Our frontend code uses React with TypeScript and standard patterns that any competent frontend engineer can pick up in an afternoon. Our backend code uses Golang or Python with explicit error handling, tested database migrations, and a deploy process that fits on one page. We avoid clever abstractions that save twenty lines today and cost twenty hours next year. The goal is software that you can hand off to a different team in three years and they can understand within a week, which is exactly what has happened with some of our earlier projects as clients grew and built internal engineering capacity.

Pricing is either fixed-scope or time-and-materials depending on what makes sense. Fixed-scope works well when the requirements are stable and the risk can be bounded, such as a well-defined integration or a production-ready MVP of a known feature set. Time-and-materials works better when the work is exploratory, the requirements will evolve as we learn, or the engagement is an ongoing relationship rather than a one-time delivery. We will recommend the model that fits your situation during scoping and explain the tradeoffs rather than defaulting to whichever is more profitable for us.