APPROACH

Rigorous engineering. No shiny demos.

We don't sell AI transformations. We build systems that do specific things, reliably, and that someone can maintain after we leave the room.

Four steps, one loop

  1. 01

    Scope

    First conversation is free. We understand the real problem, not the stated problem. We identify where the value lives (a specific workflow), where the risk lives (technical, organizational, adoption), and we frame the first hypothesis of a solution.

    Output of scoping: a short written proposal with problem, hypothesis, expected output, price, timeline. No 40-slide deck.

  2. 02

    Prototype

    We build a working prototype, not a mockup. In two to four weeks the system runs the task end-to-end on a real client dataset (under NDA). The prototype answers three questions:

    1. Does the model understand the domain?
    2. Does the workflow work end-to-end?
    3. Would real users accept it?

    If the answers are no, we stop here. It's much cheaper to find out now than after six months.

  3. 03

    Build

    On top of a prototype that already works, we build the production system. That means: tested code, structured observability, ongoing evaluation, explicit error handling, documentation, integration with the systems you already run.

    This phase typically lasts two to four months. It's where "demo that works" becomes "system that can be maintained".

  4. 04

    Measure & refine

    The production system generates data. That data tells us whether it's working, where it's failing, where it should be improved. We iterate in short cycles on evidence, not opinion.

    The loop closes here — measure → refine returns to research and build for new iterations or adjacent systems.

What we refuse

  • Demos that will never live in production. They’re a poor way to spend your budget and our time.
  • Tool reselling. We don’t resell software licenses, and we don’t have commercial partnerships with AI vendors. Our incentive is to build systems that work, not to sell you something else.
  • Scope creep. If a separate problem emerges during a project, we discuss it openly as a separate engagement. No “while we’re here”.

Frequently asked

  • Why do you always start with a prototype instead of going straight to production?
    Because in applied AI, technical feasibility and business value aren't obvious in advance. A prototype costs a fraction of the production system and answers the questions that matter: does the model actually understand the domain? Does the workflow work end-to-end? Would real users accept it? Going straight to production without those answers is the most expensive way to discover them.
  • What does 'measurable workflow' mean?
    A workflow is measurable when we can define a number — time saved, accuracy, throughput, cost per task — and track it before and after the AI system goes live. Without measurability we have no way to tell if the system is working, and we can't improve it. It's the first thing we address in scoping.
  • Do you build custom solutions or reuse existing frameworks?
    We reuse aggressively. The 'custom' part of a well-applied AI system is the prompt engineering, the retrieval, the evaluation, and the integration with client systems. The rest — orchestration, observability, deployment — uses mature open-source tools where possible (LangGraph, Graphiti, OpenTelemetry). Building frameworks from scratch is almost always the wrong answer.
  • What happens after launch?
    Production AI systems require active maintenance: models change, data changes, edge cases only surface after weeks of real use. We offer light monthly retainers for evolution and operations, or structured handoff with documentation and training if the client has an internal team. We decide together what makes sense.