AI & Automation
MLOps & LLMOps
Keep AI systems accurate, cheap and accountable in production
- Evals in CI, not vibes in a meeting
- Token and latency budgets per feature
- Full request tracing and audit trail
- 30-55%
- Inference cost reduction
- 100%
- Prompt changes scored before merge
- < 5 min
- Time to detect a quality regression
Typical after routing, caching and prompt optimisation
Evaluation suite runs in the pipeline
Alerting on eval and confidence drift
The failure mode nobody plans for
AI systems do not fall over. They drift. Accuracy slides from 94% to 81% across a quarter because the input mix changed, a provider updated a model, or three prompt tweaks each helped one case and quietly broke four others. Nobody notices until a customer complains, and by then there is no baseline to compare against.
The fix is unglamorous and it is the same fix as every other maturing engineering discipline: measure it, put the measurement in CI, and alert when it moves.
What we put in place
An evaluation suite that runs on every change. Real cases with known-correct answers, versioned with the code. A pull request that drops accuracy fails, the same way a pull request that breaks a unit test fails.
Tracing with cost attached. One view per request: prompt, retrieved context, tool calls, output, latency and money spent. Without this, debugging an AI system is guesswork with a large bill.
Budgets and guards. Per-feature token budgets, rate limits, timeouts and fallbacks when a provider has a bad afternoon. A single runaway loop should cost you an alert, not a five-figure invoice.
A version registry with rollback. Prompts, models and retrieval configs are versioned artefacts you can roll back in minutes, because at some point you will need to.
Governance comes free
The same tracing that makes debugging possible produces the audit trail your risk function will eventually ask for: what was asked, what context was retrieved, what was answered, by which model version, and who saw it. Building it in from the start costs almost nothing. Retrofitting it under audit pressure is a project.
How it runs
What the engagement looks like
Phases, not a proposal. Each one has an output you can see.
- 1
Establish the baseline
Weeks 1-2We build a labelled test set from real traffic and score what you have today. Until that number exists, nobody can tell whether a change helped.
- 2
Wire tracing end to end
Weeks 2-3Every request traced from input through retrieval and tool calls to output, with cost and latency attached. This is what turns "the AI is being weird" into a diagnosable incident.
- 3
Gate the pipeline
Weeks 3-4The evaluation suite runs on every pull request and blocks merges that regress accuracy. Prompt engineering becomes a normal engineering activity with a red or green result.
- 4
Optimise the unit economics
Weeks 4-6Route simple steps to smaller models, cache aggressively, trim context. We have not yet seen a first-generation AI feature that could not be made materially cheaper without losing quality.
- 5
Hand over the operating model
Week 6Runbooks, alert thresholds, on-call guidance and a weekly quality report your team owns.
Proof
Where we have done this
FAQ
Questions we get asked
Talk to someone who does mlops & llmops
Thirty minutes with an engineer who has delivered this, not an account manager. You will get a straight answer on feasibility, rough cost and where it would fail.
Or email [email protected] · we reply within 1 business day