Hire AI talent
into your team.

Often you should do both, and we'll say so. In-house hiring wins when the work is permanent, central to your product, and you can afford the months a senior AI search takes plus the risk of getting it wrong in a market where everyone is fishing the same small pond. Hiring through us wins when you need senior capability moving in weeks, when the workload may change shape, or when one profile isn't enough. Most AI work needs slices of several skills, which is brutal to hire for individually but trivial to assemble from a bench. Plenty of clients use us as the bridge while they build the permanent team.

A freelancer is one person carrying all the risk of one person: their availability, their bus factor, their motivation on week nine. Hiring through us gets you the individual plus the machinery around them: a named PM, weekly sprint cadence, QA before work reaches you, documentation as a habit, and an organisation that is contractually on the hook for delivery. If your engineer is ill, on holiday or simply not right, that's our problem to solve, with the bench behind them. The trade-off is honest: a freelancer can be cheaper per hour; nobody refunds you for the sprint that quietly went nowhere.

They are on our delivery bench, not sourced for the occasion: our confidentiality and IP agreements cover them and flow through to yours. You set direction and work with them day to day; the engagement terms, including who holds the contract and the notice period, are stated in the proposal. Everything they build for you is work-for-hire, assigned to you in writing. And because they stay inside our delivery organisation, they keep peer review and technical escalation behind them: hiring one person needn't mean losing the second opinion.

Brief us, and you'll have a written response, roles, rates, availability, inside 48 hours, with a shortlist typically following within days; that's the standard we run on rather than a contractual stopwatch. The commitment we do put in writing is the one we make on every engagement: first deliverable inside 14 days of the NDA. Because shortlists come from our existing bench rather than a cold search, the usual months of sourcing simply aren't part of the timeline.

Yes, across the range this site describes, because it's the same bench that delivers it: LLM integration and prompt engineering, retrieval/RAG pipelines, evaluation and guardrails, MLOps and deployment, data engineering, and the full-stack work, APIs, integrations, front ends, that turns a model into a product. We're deliberately model-agnostic and cloud-agnostic; people join your stack rather than importing ours. If you need something we can't staff well, we'll tell you at the brief rather than learning on your invoice.

Tell us, ideally in the weekly cadence where mismatches surface early. We replace the profile from the bench, with a real handover funded by the pod's documentation habits. No renegotiation, no starting the relationship from zero: the engagement is with the team, and the team is the continuity. What we deliberately don't promise is instant, frictionless swaps of infinitely available specialists. That's a brochure claim, not an operational one. What we do promise is that a wrong fit is our problem to fix, and that the first sprint is judged on shipped work, so most mismatches surface early. The proposal names the backup on the bench and states whether their cover is included in the rate.