LLMOps & AI managed services
Keep your production AI system boring.
You shipped it. Now it has to keep working at 10x the traffic, on next quarter's model, without a 2am page. That's a different job from building it — and the one most teams underfund.
Signals we track
- On target
- Watching
- Act
- What is measured
- An eval suite runs on every prompt and model change; in production, a sample of answers is scored.
- When it changes state
- Watching: The sampled score drops against the baseline.
- Act: A prompt or model change fails the evals.
- What we do
- The change never reaches users: the build fails. On a model swap, the new version stays behind a flag until the evals pass.
- What is measured
- p50, p95 and p99 latency per AI feature.
- When it changes state
- Watching: p95 is approaching the agreed target.
- Act: p95 stays above target, sustained.
- What we do
- Find where the time goes — model, retrieval or network — and act: caching, a faster model for simple requests, a load test before the next peak.
- What is measured
- Cost per request and per outcome, per feature, against the agreed ceiling.
- When it changes state
- Watching: Cost per request rises while volume doesn't.
- Act: The month is on track to break the ceiling.
- What we do
- Find the cause — a bigger prompt, more calls, a pricier model — and fix it with caching, model-tier routing and per-request budgets. Every optimisation is documented.
- What is measured
- Error and fallback rates per feature, with alerts to whoever is on call.
- When it changes state
- Watching: Fallbacks increase, but the service keeps answering.
- Act: The error rate crosses the threshold agreed in the SLA.
- What we do
- The on-call runbook: graceful degradation, recovery and a heads-up to whoever needs to know. Then a postmortem and the fix so it doesn't repeat.
Every month: a review of the telemetry, what shipped and the next backlog.
Example states and thresholds, not readings from a real system. Each system's targets come out of the audit and the baseline.
Selected work
Case study· United States
Medical · Precision-nutrition decision platformAI-driven feeding platform for a US neonatal MedTech company.
- Problem
- Preterm care is unforgiving. The cognitive load is enormous.
- Approach
- The dataset is the intelligence core. The application's job is to make it queryable at the point of care.
- Outcome
- From disconnected data to actionable intelligence at the bedside.
- 94%Protocol adherence rate post-launch
- 3×Faster feeding-decision time
- EpicEHR integrated via HL7 / FHIR
Case study· Luxembourg
Right-On-Skill · Nearshore tech team for an AI training companyTech Lead + Frontend Developer extending the engineering team for an AI training platform.
- Problem
- Scale the team without diluting architectural ownership.
- Approach
- We diagnose before we propose. Skilled talent placed in two weeks, fully aligned.
- Outcome
- Architectural foundation ready to scale. AI layer plugs in cleanly.
- 2 wksFrom briefing to first developer integrated
- 0Rejected CVs — right-fit on first match
- –70%Reduced reliance on external suppliers
- Latency tracked per AI feature
- p50/p95/p99
- Prompt and model changes eval-gated
- 100%
- Not juniors. Not a ticket queue. Principal engineers only.
- Senior
- Audit turnaround; baseline in the first weeks
- Days
- Review of telemetry, what shipped and the next backlog
- Monthly
Everything you need.To stay boring.
No vanity dashboards. No assumed quality. Real numbers, gated changes and a reliability floor — so the system holds at 10x and the bill stops climbing.
Visibility and cost
A telemetry dashboard you own
Live cost, latency (p50/p95/p99), error and fallback rates per AI feature. You stop guessing about your own system.
An AI bill that stops climbing
Cost-per-request and cost-per-outcome tracked and driven down — or held flat as volume grows — with every optimization documented.
Quality through every change
Quality measured, not assumed
An eval suite that gates every prompt and model change, plus sampled live scoring, so drift is a caught number instead of a customer email.
Model upgrades that don't break you
When a provider deprecates or releases, we re-run evals and ship the swap behind a flag — no quality regression, no scramble.
Reliability and headroom
A reliability floor
Fallbacks, retries, graceful degradation, an on-call runbook, and a postmortem after every incident. Fewer pages, faster recovery.
Headroom proven before you need it
Load-tested capacity numbers and an infra and quota plan sized to where you're going, so the next 10x is a config change, not a rewrite.
How we work.Audited, then operated.
Five steps from a cold production audit to a system that runs on a cadence — with real numbers before we touch anything.
- 01AUDITProduction audit
A senior engineer reviews the live system: what's instrumented, cost-per-request, latency profile, eval coverage, failure modes, deprecation exposure. You get a written findings doc with risks ranked. We'll audit a system we didn't build.
- 02BASELINEInstrument and baseline
We stand up the observability and eval harness so there are real numbers before we change anything. You can't claim a 40% cost cut without the baseline. This lands in days, not weeks.
- 03STABILIZEStabilise
We close the ranked risks from the audit — fallbacks, retries, the eval gates, the runbook, the obvious cost wins. The goal is to stop the bleeding and make incidents rare and recoverable.
- 04OPERATEOperate on a cadence
Monitoring is live, evals gate every change, model upgrades are handled as they come, and a monthly review shows the telemetry, what shipped, and the next backlog. The SLA is in force.
- 05OPTIMIZEOptimise and stay scale-ready
We keep driving cost-per-outcome down, hold latency as volume grows, and load-test ahead of known growth events. If you want to take operations in-house, the handover is part of the deal.
We're not for everyone.We’re for teams ready to keep it boring.
If any of these sound familiar, we should talk.
We built it and now it's eating us
An LLM feature live, with no controls behind it
You're dealing with
- A bill that climbs every month
- Latency that's a mystery
- Quality complaints arriving from customers
What you're after
You need instrumentation and cost control, not a rebuild.
Outcome
A system you can see, priced and held under control.
You built it, keep running it
Shipped with us, one or two systems to maintain
You're dealing with
- No wish to hire a full AI ops team
- Only one or two systems to keep alive
- Wanting the same engineers who scoped it
What you're after
You need operation handed back to the engineers who shipped it, on a standing arrangement.
Outcome
Operation handed back to the team that built it, no new hires.
It can't survive the growth coming
A launch, a market or 10x users ahead
You're dealing with
- A system that's fine today
- A growth event that will break it
- No headroom proven for what's coming
What you're after
You need load testing, infra sizing and reliability work before the growth event, not during it.
Outcome
The next 10x handled before it arrives, not during the outage.
Senior engineers. No handovers. No fluff.
Frequently asked questions
It starts with a fixed-scope production audit so neither side commits blind. The standing engagement is monthly, sized to the system's real surface area — features, traffic, SLA tier — not a headcount you rent. It's month-to-month after stabilisation; no annual lock-in to start.
The audit is days. Instrumentation and the first baseline land within the first weeks, so you see real numbers early. SLAs are agreed up front and tiered to what the system needs — response, resolution and uptime targets where we control enough of the stack to commit to one.
We won't claim uptime for parts of the stack outside our control, and we'll say so in the SLA. We won't run this as a ticket queue with juniors. And if the audit finds the architecture can't scale, we'll tell you it needs a rebuild instead of billing maintenance forever.
The other services get a system into production. Support & Scale is the only one whose entire job is after launch — keeping a live system reliable, cheap and current. Two tells: it's the one service we'll take on a system we didn't build, and it's measured in held SLAs and declining cost, not features shipped.
Start your deployment.
Talk directly to a principal engineer.
No sales team.
No discovery workshops.
No procurement circus.
We scope, build and ship.
- Reply within one business day
- Engineer-led assessment
- Written proposal
- Lisbon · UK time zone
No commitment. Just an engineer.