AI & Intelligent Systems
The boring infrastructure that keeps AI impressive
The platform layer that keeps AI systems reliable, measurable, and affordable at scale.
AI that works in week one and degrades by month six wasn't engineered — it was demoed. Models drift, prompts rot, costs creep, and nobody notices until a customer does. MLOps is the discipline that makes AI a system instead of a stunt.
- AI releases gated by evidence, not vibes
- Degradation caught by dashboards before customers
- Unit economics that survive scale
Capabilities
What the work actually involves
LLMOps platforms
Prompt versioning, model routing, response caching, and rollout controls for LLM estates.
Evaluation & regression testing
Automated quality gates on every change — accuracy, safety, latency, and cost as first-class metrics.
Model serving & scaling
Inference infrastructure sized to your load curve, from serverless bursts to GPU fleets.
Drift & quality monitoring
Production behaviour watched continuously, with alerts before users feel the decay.
Cost governance
Token budgets, caching strategy, and model-tier routing that cut AI bills 30–70% without quality loss.
Model and prompt registry
Every model, prompt and configuration versioned with the evaluation score it shipped against, so what is running in production is a known artefact rather than a deployment memory.
Reproducible pipelines
Training and indexing runs that can be re-executed to the same result, with data snapshots and lineage — the difference between diagnosing a regression and guessing at it.
Deliverables
What you are handed.
Yours to keep, and written so another team could pick them up.
A reproducible training pipeline
Same data plus same code equals same model — everything else depends on it.
Model registry and lineage
Which model is live, what trained it, who approved it, and how to get back to the previous one.
Serving infrastructure with autoscaling
Sized to real traffic, with the cost per thousand inferences known.
Evaluation gates in CI
A model that regresses on the test set does not reach production, regardless of who is asking.
We do not publish prices — scope drives them. Everything else, here.
- Starts with
- An audit of how models currently reach production
- Typical duration
- 6–12 weeks
- Who you get
- An ML platform engineer and an SRE
- Commercial model
- Fixed-scope build, optional retained operations
How to decide
What the answer depends on.
Two sets of conditions. Read both against your own situation — most organisations recognise themselves in one column within a sentence or two.
This is the right call when
- You have models in production and no reliable way to update them.
- Nobody can say for certain which model is currently serving.
- Retraining is a person following notes.
A different approach fits better when
- You have no models yet — this is infrastructure for an existing practice.
- One model, retrained annually. The overhead will not pay for itself.
- No appetite for gates that can block a release.
Before you ask
Questions about MLOps & AI infrastructure
Sectors
Where this comes up most.
The regulatory context and the systems already in the building change the build. Each sector page says how.
Next step
Bring us the problem. We will bring the architecture.
A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.
- No sales deck
- An engineer on the call, not an account manager
- NDA before you share anything