A senior AI team, on tap
Senior AI engineers, strategy, and MLOps embedded in your team on a monthly retainer, shipping your roadmap to production and scaling up or down as it shifts.
AI capacity without the headcount
The fractional AI team is senior capacity without the hiring cycle: engineers, an architect, and MLOps working inside your team on a monthly retainer. We join your standups, commit to your repos, and follow your review process. From the outside it looks like your team got bigger, because it did.
The first weeks are onboarding and an audit of what you already have: models, pipelines, evals, infrastructure. From there we take ownership of an agreed slice of the roadmap and ship in your normal cadence. Capacity is set each month, so you can add engineers for a push and dial back once things settle.
Over time the point is to make ourselves less necessary. We build the MLOps and quality gates that keep models in production without heroics, document as we go, and pair with your engineers so the knowledge lands in your team. When you hire your own people, they inherit a working system, not a mystery.
What you get
How it works
Onboard and audit
We get access, join your standups, and audit what exists: models, data pipelines, evals, and infrastructure. You get a written picture of where things stand.
Own the roadmap
We agree which slice of the AI roadmap we own, with clear interfaces to your team. Priorities are set together each month, in your planning, not in a vendor portal.
Ship in cadence
We deliver in your sprint rhythm: design, build, review, release. Work lands in your repos through your review process, with a demo of shipped work every cycle.
MLOps and quality gates
We build the plumbing that keeps AI in production: evals in CI, monitoring, rollback paths, and cost tracking. The quality gates hold whether we are in the room or not.
Review and hand over
Every quarter we review outcomes against the roadmap and adjust capacity. As your own team grows, we pair, document, and hand over ownership piece by piece.
Who it's for
How to prepare
The engagement starts fastest when access and ownership are clear on day one.
Questions, answered
How fast can you start?
Days, not months. We onboard into your stack and standups in the first week and ship from there.
Can we scale up or down?
Yes. Adjust monthly capacity as your roadmap shifts: add engineers for a push, dial back when things settle. No long lock-in.
How is this different from hiring?
No six-month search, no ramp-up risk. You get a senior team already fluent in production AI, strategy, and MLOps, available this month.
Who owns the code and models?
You do. Everything lives in your repos and your infrastructure. We build inside your stack, so nothing walks out the door when we scale down.
How is this different from hiring contractors?
Contractors give you individual hours you have to manage. We come as a coordinated team with an architect, engineers, and MLOps, own outcomes rather than tickets, and bring our own delivery standards, evals, and review discipline.
What is the minimum commitment?
Long enough to judge us on shipped results: we suggest a first quarter for onboarding, the audit, and the first outcomes in production. After that it runs month to month, and you scale down or stop with a month's notice.
Talk it through
Every retainer starts with one call: your roadmap, your stack, and whether we are the right team for it. Thirty minutes with an engineer, no sales deck.
Book a callBook a fit call
30 minutes with an engineer, no sales deck.
- Senior AI engineers shipping in your repos from week one
- An honest audit of your models, pipelines, and infrastructure
- A slice of the roadmap owned end to end, from design to production
- MLOps, monitoring, and quality gates that outlast the engagement
- Monthly capacity you can scale up or down as priorities shift
- Documentation and pairing that grow your own team's skills
- 1We match you with the right engineer.
- 2You hear back within one business day.
- 3A 30-minute call to scope it.