IT Staff Augmentation Services & Dedicated AI Engineering Pods

In a project engagement we own the delivery and hand you a result. In staff augmentation you own the delivery and we supply engineers who work inside your team, on your backlog, under your management. Choose augmentation when you have the direction and process but need more skilled hands, especially in AI roles that are hard to hire.

AI engineers, machine learning engineers, LLM engineers, MLOps engineers, and data engineers, at senior and principal levels. We match to the specific skills your work needs rather than sending generalists. If you need a role we would not staff well, we say so rather than fill the seat, because a poor match costs you more than an empty one.

Faster than direct hiring, usually weeks rather than months, because the vetting is already done. We shortlist against your requirements, you interview, and the engineer onboards into your sprint. The exact timing depends on the seniority and specialism, which we are honest about up front rather than promising an unrealistic start date.

Through technical assessment of real skills, not just interviews, plus a track record on production AI systems. We staff people we would put on our own builds. The point of augmentation is that the vetting risk sits with us, so you gain a proven engineer without running a long hiring pipeline for a scarce specialism.

You do. Everything the engineers produce is yours, under clear contract and IP-assignment terms, and they work under your NDA and security policies. We make ownership explicit from the start, because ambiguity there is where staffing arrangements go wrong. There is no lock-in to us in the work they deliver.

They join your standups, your board, and your code review, and work to your definition of done. Augmentation only works if the engineers are part of your team, not a separate silo. We brief them to adapt to your ways of working rather than importing ours, so integration is smooth and the rest of your team feels the added capacity, not friction.

Usually a monthly rate per engineer or per pod, on a rolling term you can scale up or down with notice. This keeps it flexible: add capacity for a push, reduce it when the work settles. We are transparent on rates and what changes them, so there are no surprises, and you are not locked into headcount you no longer need.

Yes. A pod is a small, ready-made team, for example an ML engineer, an MLOps engineer, and a data engineer, that plugs into your program with its own internal coordination. A pod suits a defined workstream you want owned end to end within your wider effort, while individual engineers suit filling specific gaps in an existing team.