Service 01
AI Systems
AI that sits inside an operational process and is accountable for an outcome, with an evaluation set behind every claim it makes.
- Capability 1
- Agents
- Capability 2
- Retrieval
- Capability 3
- Computer vision
- Capability 4
- Forecasting
The approach
Most AI projects fail at the same point: nobody agreed what success would be measured against, so there is no way to tell whether a change made things better. We start with that number and build backwards from it.
That means the evaluation set comes first, before the prompt, the model choice or the retrieval strategy. A few hundred real inputs with the answer you would accept next to each one. It is the artefact that lets you change anything afterwards without fear.
It also means we will sometimes tell you the model is the wrong tool. Deterministic logic is cheaper to run, faster, testable in CI and explainable to an auditor. Where a rule beats a model we ship the rule, show you the comparison, and spend the budget somewhere it earns more.
- 01
Establish the target
We read the process as it runs and agree the metric. If the historical data does not support the brief, you hear that in the first fortnight.
- 02
Baseline, then model
The simplest possible approach is measured first. The model has to beat it on the same holdout, in the same units, to justify itself.
- 03
Ship with a fallback
Staged rollout behind a flag, a deterministic path underneath, and monitoring that compares production against the evaluation set.
What you get
Deliverables, not a status update.
- 01
Evaluation harness
A held out set, a grader and a scoreboard you own, so quality is a number rather than an opinion.
- 02
The system itself
Retrieval, agents, vision or forecasting, deployed inside your process with a deterministic fallback path.
- 03
Cost and latency budget
Measured per request, with model routing tuned against it rather than against a benchmark.
- 04
Failure behaviour
What it does when it is unsure, when a provider is down, and when the input is nothing like training.
- 05
Monitoring
Online measurement against the same metrics as the offline set, so drift is visible before a user reports it.
- 06
Written rationale
Why this architecture, what was rejected, and what would make us change our mind.
Questions
Answered straight.
- Which models do you use?
- Whichever fits the task, and we keep re-testing that choice. We are not tied to one vendor, and routing is tuned against your cost and latency budget rather than a leaderboard.
- What if our data is not ready?
- That is common and it is usually the real project. We will say so early rather than build on top of it and hope.
- Can this run in our own environment?
- Yes. We build for tenant isolation, data residency and audit logging as a starting assumption, not a later hardening pass.
Start here
Have an AI systems problem?
You will speak to an engineer, and you will leave the first conversation with an opinion about your problem whether or not you hire us.