What we actually do, in detail.

The bulk of our recent work. We build AI features that earn their place in a real business workflow — assistants that pull from your knowledge base, pipelines that classify and route documents, copilots that sit inside the tools your team already uses. We’re much less interested in chatbot demos than in the messy second half: how the system fails, how it stays grounded in your data, how a human takes over when it shouldn’t.

Most projects use Claude or GPT as the underlying model, with retrieval against your own data and a small amount of glue code that turns the language model into a reliable component of a larger system.

Where we don’t go: we don’t train foundation models, we don’t build “general AI assistants” without a defined workflow, and we’re cautious about agentic systems with no human in the loop — for good reasons.

Web applications built around how a specific team actually operates. Customer portals, internal tools, dashboards, light SaaS products. Boring stack on purpose — TypeScript, Postgres, a sensible framework — so your engineers can keep maintaining it after we hand over.

Connecting the systems you already pay for. The unsexy middleware between CRM, ERP, billing, support, and inbox is often the single highest-leverage piece of software a mid-market team can have built — and the one nobody enjoys writing.

One honest note: simple integration is often best done in a no-code tool. We’ll tell you when that’s the case rather than build something custom you don’t need.

The infrastructure underneath everything else — usually paired with one of the work types above. We deploy on AWS, Google Cloud, or Cloudflare, depending on what the workload actually needs and what your team can operate.