Operational monitoring
Quality sampling, latency percentiles, error rates and cost tracked continuously.
Running it after launch
Managed AI Services
Ongoing operation, tuning and cost control for AI systems in production.
What this is
An AI system needs someone watching it. Content goes stale, providers change models, question patterns shift and costs drift. We operate AI systems as a service: monitoring, tuning, content and index maintenance, cost control and a defined response path when something degrades.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- An AI system launched, then slowly got worse and nobody owned it.
- The internal team that built it moved on to other priorities.
- Model spend climbs each month with no attribution or control.
- Provider model deprecations arrive with no plan to migrate.
- Nobody reviews what the system failed to answer.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Monitoring and alerting on quality, latency, error rate and cost
- Scheduled evaluation runs with regression reporting
- Prompt and retrieval tuning against observed failures
- Knowledge base and index maintenance, including re-indexing and freshness
- Cost review with routing and caching optimisation
- Model migration handling as providers deprecate and release versions
- Monthly report covering performance, incidents, gaps and recommendations
- Defined response times for degradation and incidents
Capabilities
What the system does
Continuous evaluation
Scheduled runs against your test set, extended as new failure modes appear.
Tuning and improvement
Prompt, retrieval and routing changes driven by real failures, tested before release.
Content and index maintenance
Re-indexing, freshness checks, removal of superseded sources and gap remediation.
Cost management
Attribution, routing review, caching and budget alerting.
Model lifecycle
Deprecation tracking, candidate evaluation and migration with evidence quality held.
Incident response
Defined response times, runbooks, rollback and post-incident reporting.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Post-launch operation
Keeps a delivered system healthy when there is no internal team to own it.
02
Augmenting an internal team
Provides AI-specific operational depth alongside your existing engineers.
03
Taking over an existing system
Assessment, stabilisation and ongoing operation of a system built elsewhere.
04
Cost control programme
Ongoing optimisation where model spend has become material.
05
Knowledge base ownership
Continuous content and index maintenance for a RAG deployment.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Monitoring layer
Metrics, traces, quality sampling and alerting across the AI request path.
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02
Evaluation layer
Scheduled and change-triggered runs against a maintained test set.
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03
Change process
Versioned prompt and configuration changes, evaluated and deployed with rollback.
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04
Content operations
Index freshness, source review, re-indexing and gap remediation.
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05
Cost operations
Per-feature attribution, routing review, caching and budget enforcement.
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06
Reporting
Monthly performance, incident, gap and recommendation reporting.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Access scoped to what operation requires, under your identity and audit controls.
- All changes versioned, evaluated and reversible.
- Agreed response times for degradation and incidents, stated in the retainer.
- Monthly reporting on quality, cost, incidents and outstanding risks.
- Exit plan with documentation and handover, so the retainer is not a dependency trap.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
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Questions
Managed AI Services — FAQs
The questions we are asked most often about this service.
Do you only manage systems you built?
No. We take on systems built elsewhere, starting with an assessment and a stabilisation phase so we understand what we are operating before committing to response times.
What response times do you offer?
They are agreed per retainer against your business hours and the criticality of the system, and written into the agreement. We do not advertise a blanket round-the-clock commitment we have not scoped with you.
Can we take it in-house later?
Yes, and the exit path is part of the agreement. Documentation, runbooks and handover are maintained throughout, not assembled at the end.
How is this priced?
A monthly retainer scoped to the number of systems, their volume and the agreed response times. Larger improvement work is quoted separately rather than absorbed silently.
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