AI MANAGED SERVICES
Managed services for production AI
We support production AI through monitoring, incident response and cost optimisation, with responsibilities and service targets agreed for each system.
- Drift and cost anomalies caught before a business owner notices
- Managed to cost-per-task, not the monthly GPU bill
- A persistent pod that knows your systems, against agreed SLAs
Production AI, run the way we run production software.
With SLAs, observability, and a persistent team that knows your systems. Pipelines are instrumented to catch drift and cost anomalies early, and we act within agreed service levels rather than waiting for a complaint.
Delivered through our Global Capability Centre model, the same pod compounds knowledge of your environment over time. Support gets sharper and faster instead of resetting with every ticket.
Pipeline optimisation and monitoring
Detect drift, failures, and quality decay, then tune continuously so issues surface before users feel them.
AI platform support
Maintain availability, apply updates, and resolve issues on the platforms your teams build on.
AI FinOps
Make AI cost visible and controllable, then drive it down without degrading output.
Managed AI is not "keep the servers up."
The highest-cost mistakes in enterprise AI happen after launch, not before it.
These are the five specific decay modes we watch and act on, each with its own detection signal and its own remedy.
Data and concept drift
The world moves and the model's assumptions age. Accuracy slips long before anyone files a ticket about it.
Embedding and retrieval rot
RAG indexes go stale, so grounded answers quietly get less grounded while the system still looks healthy.
Cost creep
Token-heavy contexts, oversized models, and agent retries inflate the bill without anyone deciding to.
Quality regressions
A prompt or model change fixes one case and breaks ten in the long tail, where nobody is looking.
Guardrail gaps
New inputs find edges the original guardrails never covered, and the first sign is usually an incident.
The highest-cost mistakes in enterprise AI happen after launch.
Most AI overspend is recoverable.
We work the levers in order of return-on-effort, starting with the ones that pay back in days.
Baseline cost per completed task, test changes against the same quality criteria, and report measured savings after accounting for changes in traffic and workload.
We can take responsibility for AI your team or another partner built.
Assess
Inventory the systems, pipelines, cost profile, and gaps as they actually stand today.
Instrument
Add monitoring, evaluation, and cost observability so decisions rest on evidence.
Stabilize
Fix the drift, quality, and cost issues the assessment found, then set SLAs against them.
Operate and improve
Steady-state run with continuous optimisation on both accuracy and cost.
The transition establishes the evidence, controls and service levels needed to operate it responsibly.
Platform engineering for a live automotive data service.
Datium Insights · Automotive and Data · MONITORING AND FINOPS99.99%
A valuation platform that had to stay up while it got faster.
We developed Datium Insights' automotive data products and commercial platform on Azure, reducing valuation response time from 22 seconds to 5 while supporting 99.99% availability. The engagement demonstrates our platform engineering experience with demanding latency and availability requirements.
What operations leads ask us first.
Yes, and that is the common case. We assess the existing systems, pipelines, and cost profile, then take on monitoring and support against agreed SLAs regardless of who built them.
It makes AI cost visible and controllable in cost-per-task terms, then works the levers: caching, routing, right-sizing, quantization. Run-rate comes down, often materially, without degrading output.
We monitor input data, predictions, and outcome metrics against baselines, using proxy and LLM-judge metrics where ground truth is delayed, and alert when they move beyond thresholds so we can retrain or adjust early.
SLAs are set to the criticality of each system. Availability, response, and quality targets are agreed up front, with a structured transition plan to reach steady state.
What we are writing about running AI.
How is your production AI performing? And what is it really costing?
Let's review both, against the metrics that matter, and put an owner on the answer.