From data reality to production system

Mapped to where you are in the stack

Scoping & data audit

Pipelines & schema design

Training, evaluation & iteration

Deployment & systems integration

Weeks of structured discovery: problem statement, data inventory, and a written feasibility assessment before any architecture decisions are made.

Data model, ingestion pipeline, labeling strategy, and quality contracts. The infrastructure that determines whether a model can learn anything useful.

Model selection, fine-tuning, and evaluation against the business metric that actually matters — not accuracy on a held-out set, but performance in your workflow.

Inference infrastructure, API contracts, monitoring hooks, and integration into the existing systems your team already operates and maintains.

Weeks of questions before a line of code

Most AI projects fail because the problem statement was wrong. We run a structured scoping phase — data audit, feasibility review, metric definition — before any development contract is signed.

Every service is measured against a business metric you name at the start, not a benchmark we choose at the end. If the data can't support the system, we say so in week two.

Problem definition workshops, data inventory, and a written feasibility assessment. No code written. No architecture decided. Deliverable: a go / no-go recommendation.

Data pipeline design, schema contracts, and an integration specification signed off before development begins. The plumbing gets designed on paper first.

Iterative model development evaluated against the business metric defined in phase one. Failures are documented; the ones that didn't ship inform the ones that do.

Deployment into your existing infrastructure, monitoring configuration, and a handoff runbook your team can actually operate without us on retainer.

Tell us the problem. We'll tell you if we can help.

We take a limited number of engagements per quarter. First call is a scoping conversation — no pitch deck, no sales process.