We help organisations run AI services they can trust.
Most AI work stops at the demo. Someone builds a clever prototype, the room nods, and then six months later a quieter conversation starts: is this thing still doing what we think it's doing? Who owns it when it breaks? How do we know the outputs are accurate? What happens when the model updates? That gap — between "we built something" and "we run something we trust" — is where we work.
We're a small consultancy. We do research, prototypes, working code, system prompts, and the strategy, service design, and service management that turns all of that into something an organisation can actually operate.
Who we work with
We don't have a sector. We have a type of client. The organisations we do our best work with share four habits —
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They want to understand, not just delegate. The senior people in the room read the prompts. They ask why an eval set looks the way it does. They don't want a black box, and they're suspicious of anyone offering one.
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They're comfortable with honest uncertainty. When we say "we don't know yet, here's what we'd need to find out," they hear competence rather than weakness. They'd rather have a true answer in three weeks than a confident one this afternoon.
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They take the operational layer seriously. They understand that the interesting work doesn't end when the prototype demos well. It starts when the thing has to run on a Tuesday morning, six months later, while no one is watching. They're willing to fund the unglamorous parts: evals, monitoring, governance, support tiers, cost models.
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They own the AI decisions in their organisation. They don't outsource judgement to us or to a vendor. We're the people they think out loud with. The call is theirs. If that sounds like you, we'll probably get on. If it doesn't, we're a poor fit and we'd rather you knew that now.
What we actually do
Concretely, the work tends to look like one or more of these:
We design evaluation frameworks — the test sets, golden datasets, and scoring rubrics that tell you whether an AI feature is getting better or worse over time. Without these, every model update is a coin toss.
We write system prompts and put them under version control. We treat prompts like code: tested, reviewed, changelogged. We can also train your team to do the same.
We build the monitoring and governance scaffolding that lets non-technical leaders sleep at night — dashboards for accuracy, hallucination rates, cost, and drift, plus the human-review workflows for when something looks off.
We write service management plans that turn a prototype into a service. Real ones — with SLAs that cover things like citation fidelity and concurrent user latency, not just uptime; support tiers that handle "cognitive incidents" alongside ordinary bugs; staffing models that account for AI operations as well as DevOps.
We prototype quickly when there's a question that can only be answered by building. Working code beats slideware.
We do strategy work too, but only the kind that produces a decision. We don't write reports that sit on shelves.
Get in touch
admin@sparkysquirrel.com — a sentence or two about what you're working on is enough to start.
