The half of AI work that nobody demos.
Anyone can wire a model to a text box. What decides whether an AI feature ships is the unglamorous half: does quality hold across ten thousand real inputs, what does it cost per run at scale, and what happens when it is wrong.
Ilmino's question bank is generated, independently verified and quality gated by a pipeline we built, at a scale of tens of thousands of items, alongside marking of typed and handwritten student work.
AI engineering in practice.
Evaluation pipelines
Scored test sets and independent verification, so a prompt or model change is a measurement rather than a hope.
Retrieval over your own content
Answers grounded in your documents with citations, so output can be checked rather than trusted.
Cost and latency control
Model routing, caching and batching, so unit economics work at ten thousand users and not only at ten.
Human in the loop
Review steps and audit trails wherever being wrong is expensive, which is what makes output defensible to a regulator or a customer.
Generation at volume
Producing and quality gating content at a scale manual authoring cannot reach.
The honest view.
If you cannot describe how you would measure whether the output is good, you are not ready to build the feature. That is the question we ask first, and sometimes the honest answer is that a database query would do the job.
Straight answers.
Which model providers do you work with?
Whichever fits, and usually more than one in the same product. Routing cheap models for easy cases and expensive ones for hard cases is often the difference between viable and unaffordable.
How do you stop it being confidently wrong?
You design for it rather than trying to eliminate it: grounding in your own data, evaluation sets that catch regressions, confidence thresholds, and a human step wherever the cost of being wrong is real.
Can you work on a prototype we already have?
That is often where we add most. Teams frequently have something that works in a demo with no evaluation, no cost visibility and no guardrails, which is precisely what stops it shipping.
Who owns the prompts and evaluation sets?
You do, along with the code and the data. Those artefacts are usually the most valuable thing produced, and we do not hold them.
Building in AI engineering?
Whether it is a new build, a rescue or a second pair of hands alongside your team, tell us what you need and you will get a straight answer within one working day.