AI Retainer — ongoing capability development
AI that compounds
over time.
Production AI is not a destination. Models drift. Data shifts. New problems emerge. A retainer keeps your systems sharp and your capability moving forward.
The model
Deploying a system is the start.
Going live is not the end of the work. AI systems in production require ongoing attention: models need evaluation as real-world data diverges from training distribution, prompts need refinement as edge cases accumulate, and new use cases emerge as teams experience what AI can actually do for them. Organisations that treat deployment as a finish line fall behind the ones that treat it as a launchpad.
An AI Retainer gives you a dedicated team of Forward Deployed AI Engineers operating on a monthly cycle. We run structured development sprints, monitor your live systems, respond to issues, and provide honest strategic advisory on where your AI capability should go next. The team that built your system — or knows it deeply — stays close to it.
Think of it as a senior AI engineering capability without the overhead of full-time headcount at that level. Flexible scope, deep expertise, and continuity of context that no new hire could replicate.
“The organisations that compound their AI advantage are the ones with ongoing engineering investment, not one-off projects.”
What’s included
Monthly, structured, and measurable.
Monthly
Development sprints
New features, model improvements, and integrations delivered in structured two-week cycles. Backlog prioritised jointly at the start of each month against current business priorities.
Continuous
Performance monitoring
Live dashboards, alerting, and regular health checks across your AI systems. Issues surfaced proactively, not reported by users after they’ve already affected operations.
Ongoing
Model evaluation & tuning
Systematic evaluation of model performance against your specific use cases. Prompt engineering, fine-tuning, and retrieval improvements as the data and requirements evolve.
Quarterly
Strategic roadmap
Reviews where we assess what’s working, what the model landscape has changed, and where the next highest-value AI investment lies. You stay ahead of the curve, not behind it.
As needed
Incident response
When something breaks or behaves unexpectedly, we are on it. Defined SLAs, clear escalation paths, and engineers who already understand your system and don’t need a handover to act.
Always
Frontier knowledge transfer
The AI landscape moves fast. As new models, architectures, and techniques mature, we assess them against your use cases and bring relevant advances into your capability without hype.
Who it’s for
For organisations post-deployment.
A retainer is the natural next step after a sprint or platform build — but it is also available to organisations that have live AI systems built by other teams and need ongoing expert support, monitoring, and development capacity to keep them healthy and improving.
It works best when there is a clear pipeline of future AI work and a leadership team that understands AI as an ongoing operational investment rather than a one-time capital project.
How we work
Continuity is the product.
The highest-value aspect of a retainer is accumulated context. The engineers working on your system in month twelve are the same ones who helped build it. That depth — understanding your data, your edge cases, your team’s working style — is not transferable. It is earned through sustained engagement, and it is what separates a productive retainer from a revolving door of contractors.
We maintain a living technical roadmap and prioritised backlog with your team. Monthly planning sessions ensure we are always working on the highest-value problems. Quarterly strategy reviews give leadership visibility into direction, progress, and the evolving AI landscape as it relates to your specific context.
Our retainer team spans data science, AI engineering, and system design. Cross-functional depth by design — capable of moving across the full stack rather than waiting for a specialist to become available. Communication is direct: you always know what is being worked on, what decisions have been made, and why. No surprises.
Next step
Let’s keep building.
Retainers start with a conversation about where you are and where you want your AI capability to go. No fixed scope required upfront.