An AI consulting company that builds the thing, not a deck
The standard failure is a well-argued strategy deck describing an AI programme nobody can operate, delivered by a team that will not be there to build it. We are an AI consulting company that works the other way round, sized for small and midsize businesses rather than for a two-year transformation: find the smallest version of the problem, prove it on your real data, and only then talk about scale. Sometimes the finding is that AI is the wrong tool, and we say so.
WHAT YOU GET
What an AI automation consultant works on
Ordered by how often it turns out to be the highest-value place to start, which is rarely where the initial conversation began.
Opportunity assessment
Which processes have enough volume and enough repetition to be worth it, sized against what they cost you manually today.
Feasibility on real data
Testing against your actual records rather than a clean sample. This is where most optimistic projects meet reality, and it is cheap to find out early.
Build or buy
Most needs are met by a tool that already exists. We have no incentive to build when configuring something off the shelf is the right answer.
Implementation
The build itself, connected to the systems already holding your data, with evaluation and guardrails on anything a model decides.
Adoption
A system nobody uses is a write-off. Rollout runs through the people whose work changes, not around them.
Ongoing operation
Models drift, APIs deprecate, processes change. Someone has to own it after launch, whether that is us or a named person on your side.
Related: AI implementation services, AI consulting for small businesses, workflow automation, AI agent development, the Unlisted Homes case study, and the automation ROI calculator.
HOW IT WORKS
Smallest version first
A scoped engagement runs 4 to 8 weeks and is designed to produce one thing in production, not a roadmap.
Assess
Map candidate processes against volume, repetition, and current cost. Rank by expected value rather than by enthusiasm.
Prove
Test the top candidate against real data. Cheap, fast, and specifically designed to fail early if it is going to fail.
Build
Implement the proven version with evaluation, guardrails, and a fallback for anything a model gets wrong.
Operate
Monitoring, error handling, and a named owner. Then the next project, chosen using what the first one taught you.
The uncomfortable part of the assessment
Think of it as an AI business consultant who stays for the build. Most advice in this market stops at a recommendation. The assessment below is where ours starts.
A meaningful share of what arrives described as an AI project is not one. It is a reporting problem, a data problem, or a process with three exceptions a week that nobody has written down. Putting a model on top of any of those produces something that demos well and degrades quietly, which is worse than leaving it alone because the failure is now invisible.
The other frequent finding is that the work is real but the tool already exists. Buying and configuring something proven beats building nearly every time on cost, timeline, and who maintains it at month thirteen. We have no product to protect, so recommending you buy rather than build costs us nothing.
What is left after those two filters is usually smaller than the original ambition and much more likely to survive. That is the engagement worth having.
- Feasibility tested against your real data, not a clean sample
- Build or buy assessed honestly, with buy recommended when it wins
- Every model-driven step gets evaluation and a defined fallback
- A named owner at handover, on your side or ours
Frequently Asked Questions
Not sure where AI actually pays in your business?
Book a free AI strategy session with our AI consulting company. We will work through the candidates with you and tell you which ones we think are not worth doing.
Book a Free AI Strategy Session