We help you get full control of your tech and AI systems.
This is for you if...
You found out your AI was producing wrong answers when a customer complained.
Your dashboard shows green. But green only means running.
You have logs. Logs only tell you what happened.
Your AI has been live for months and you have never actually measured its output quality.
Every AI workflow you are running, what it touches, and what it is supposed to produce.
We assign a defined expected output to each workflow, so that it can be measured over time. Workflows nobody owns, or remembers switching on, turn up here too.
Continuous measurement of what your tech and AI is producing, with alerts when the output falls outside the thresholds you set.
We set up tracking across your tech stack, AI models included. When a key metric drifts or something fails, the right person gets an alert and can act straight away.
A record of what the AI read and what it did with it, written in sentences instead of log lines.
247 rates set overnight. One looked wrong.
The model wasn't sure about unit 119, so it kept yesterday's price and told Ops at 03:07.
You need to understand what happens in your tech. We help you build audit trails that someone in operations or compliance can follow without asking an engineer to interpret them.
What to do in the first hour after an alert fires.
We write this with you, for your business, to help you react to any failure type. This includes e.g. how to confirm it is real, how to stop it, and who needs to be alerted.
Thirty customers churned but nobody saw it happen
Hospitality AI company
A company building AI marketing agents for hospitality operators launched without monitoring, so nobody could see whether the agents were helping users post content, grow engagement or win direct bookings. All 30 early customers churned silently before anyone understood why. We defined a product analytics framework with clear metric definitions for every agent and specified lightweight PostHog-based monitoring. The second launch kept customers.
Start with a free call. From there we either run a full value iteration on your stack and hand you the plan, or we stay on as your fractional CTO for data and AI.
Free, 30 minutes
Bring one thing that's stuck. We tell you what's wrong with it and what to do first.
Book a checkpointScoped engagement, one-off
We analyse your whole stack and build a pragmatic plan: what's broken, what to fix first, and who should own it. Your team or any vendor you hire can run it.
Ongoing, fractional
We work with you a few hours a week on what moves the strategy: prioritisation, vendor calls, architecture decisions and hiring.
Output quality: whether the AI is doing what it is supposed to do on real inputs. This includes accuracy drift, unusual output patterns, and failure rates by input type. Traditional uptime monitoring tells you the service is running and catches none of that.
With a monitoring layer, you get alerts when outputs fall outside defined quality thresholds, before a customer sees a wrong answer. Without it, you find out from a complaint or a reconciliation that something has been wrong for weeks.
Seems to work is hard to defend. AI models degrade gradually as input data shifts and edge cases accumulate. The failures are often invisible until they are expensive. Monitoring replaces the feeling that things are probably fine with evidence.
Logging records that something happened. Monitoring tells you whether what happened was correct. Logs are for debugging after the fact. Monitoring catches problems before they reach customers.
For a defined AI workflow with clear expected outputs, 2 to 3 weeks for initial setup: defining quality thresholds, building the evaluation layer, connecting alerts. From there it runs continuously with minimal maintenance.