Software engineer building a healthcare AI system, reviewing code across multiple monitors in a dark office

Capability 02

AI is not the product. Removing the constraint is.

Software and AI built against a constraint you can name. If a model is not the thing that removes it, we will tell you that before you fund it. You own the repository, the cloud account and the pipeline from the first commit.

Where the work lands
  • Six weeksZero to a first version
  • Fixed scopeAnd a fixed date
  • You own itCode and infrastructure

How we build

Four rules that do not bend.

These are the reasons projects survive contact with a real clinic, a real payer and a real security review.

  1. The constraint decides, not the technology

    We start from the step that is costing you and work backwards to what removes it. Sometimes that is a model. Often it is a queue, a schema or a missing integration, and we will say so rather than sell you the interesting version.

  2. A person stays accountable for every output

    Nothing reaches a patient, a claim or a chart on a model's say so. The system prepares the work and a person approves it. That boundary lives in the product, not in a policy document.

  3. It is built to be checked six months later

    Benchmarks against frontier models, regression suites on real cases, and drift you can see before a customer does. A system nobody can evaluate is a system nobody can keep.

  4. You own the code and the infrastructure

    The repository, the cloud account and the deployment pipeline are yours from the first commit. No runtime licence, no platform you have to stay on to keep what we built.

Where the work lands

Four shapes the same discipline takes.

Which one you need falls out of the constraint, not out of a preference. Each has its own page.

A first version real users touch

Fixed scope, fixed date, about six weeks from zero to something in production. For founders and for teams inside larger organizations who need to prove a thing works before it gets funded properly.

MVP development

Clinical AI inside an existing product

Evidence retrieval, decision support a clinician can audit, and the health data integrations underneath. For teams who already have users and need the AI layer to hold up in front of them.

Clinical AI implementation

The software that has to exist

Case management, quoting engines, patient-facing apps, the internal tool the workflow depends on. Built when nothing on the market does the job and the workaround has become the cost.

Custom engineering

Retrieval over a clinical corpus

When the constraint is that people cannot find or trust what the system returns, the work is retrieval architecture rather than application code.

Healthcare RAG

Anything built here is reviewed against the same standard as work we did not build. That review is AI governance and security.

Describe the step you want to stop doing by hand.

Thirty minutes. We will tell you whether software removes it, whether AI is the right mechanism, and what a first version would take.

Thirty minutes, free, no pitch