Senior AI capability, inside your team
For teams with a hiring bottleneck: an engineer producing in week one, not ramping for a quarter. From one embedded engineer to a cross-functional pod, in your tools and your sprints from day one, month to month.
1
engineer, or a full pod — you choose the shape
4
ways the arrangement is designed to de-risk
0
long contracts: it is month to month
Four things that make it worth doing
What makes this credible is not a bench size. It is that the engineers arrive carrying the patterns this company already runs in production.
Embedded, not adjacent
Same repositories, same sprints, same observability stack as your own team. This is how we deliver everything else, so it is not a special arrangement.
Deployed with the patterns we run
The retrieval architectures, guardrail patterns, infrastructure-as-code setups and observability stacks that are already in our own production systems.
Month to month
Flexible by design, because a capability you cannot stop is a hire, and hiring is the thing you came here to avoid.
A replacement guarantee
If the fit is wrong, we replace quickly. One person carrying your whole system is the risk this arrangement exists to remove.
Agency, freelancer, or a hire?
The real trick is not who you hire. It is starting with a build small enough that you can afford to be wrong about it.
A freelancer is one person carrying your whole system: a holiday, an illness or a better offer stalls the project. An employee is a large annual commitment, and one developer rarely covers the interface, the AI work and the infrastructure anyway. An arrangement like this gives you a team and accountability, but only makes sense if it scopes small — so we scope it small.
The practice behind the person
Agent engineering
Guardrails in code, supervisor topologies, and humans at the boundary.
See the practice →Retrieval
Hybrid search, reranking and evaluation harnesses that make quality measurable.
See the practice →Cloud and DevOps
Infrastructure as code on three clouds, with cost engineering treated as a feature.
See the practice →Tell us what the bottleneck actually is
This is our AI engineering practice
It is real work and it is where our four products came from. But what Cognilium leads with is narrower: optimization apps that run in tandem with Microsoft Dynamics 365, computing the decisions the ERP records but does not derive — the optimal price, the optimal pick path, the optimal stock level. See the optimization apps · How we build inside the ERP.