Four ways we get involved
Some engagements are a single evaluation sprint. Others are a full build. Here's the shape of the work we do, and how we scope it.
AI/ML Product Development
From problem framing to a working model integrated into your product. We scope against your actual data and constraints — not a best-case demo — and build models, pipelines, and the surrounding application logic to ship them.
Typical work
Model design & prototyping, feature pipelines, data labeling strategy, integration with existing product/backend.
MLOps & Deployment
The infrastructure that turns a working model into a system your team can operate: training and retraining pipelines, versioning, CI/CD for models, and deployment targets that match your existing stack.
Typical work
Pipeline orchestration, model registry & versioning, rollout/rollback tooling, infrastructure-as-code.
Model Quality & Evaluation
Most teams find out a model regressed when a customer complains. We build the evaluation sets, test harnesses, and production monitoring that catch it first — and give you a defensible way to say “this is ready.”
Typical work
Eval set design, regression & drift testing, offline/online metric dashboards, release gating.
Applied AI Strategy
For teams deciding whether and where AI belongs in the roadmap. A focused technical audit of your data, systems, and use cases, ending in a prioritized, honest recommendation — including “don't build this.”
Typical work
Technical & data readiness audit, use-case prioritization, build vs. buy analysis, roadmap.
A process built to avoid surprises
The same four steps whether the engagement is two weeks or six months.
- 01
Scope against reality
We start with your actual data, systems, and constraints — not a hypothetical clean dataset. If something isn't feasible, we say so before you pay for it.
- 02
Define “working” up front
Before we write model code, we agree on the evaluation criteria and metrics that will decide whether the system is ready to ship.
- 03
Build, test, iterate
Short cycles with visible progress — working software and evaluation results, not slide decks, are how you track status.
- 04
Hand off, not walk away
Documentation, runbooks, and monitoring so your team can operate and extend the system after we're gone.
Tell us the problem, not the solution.
Most engagements start with a short technical conversation about what you're trying to solve — before we talk about which service it maps to.