Where AI pays for itself here, and where it doesn't.
Most AI strategy decks are interchangeable because they are written from the model outwards. The useful direction is the other one: start from the processes that cost you the most hours, check whether their inputs are written down somewhere a system can read, and price what happens when the answer is wrong. Where those three line up, automation pays back fast. Where they do not, no model fixes it.
This is for you if
- Your board has asked what the AI plan is and the honest answer is that there isn't one yet
- Every vendor you have spoken to has recommended the product they sell
- You have run a pilot that impressed everyone and changed nothing operationally
- You need to know whether to build, buy or wait, and to be able to defend the choice
It isn't, if
- You already know what to automate and want it built. Skip this and buy the build; a consulting engagement to confirm a decision is a delay you pay for.
- You want a strategy document for a board pack. We will write findings, not reassurance, and findings are less comfortable.
- Nothing about your process is written down anywhere. That is a documentation problem first, and no amount of AI consulting substitutes for it.
What actually happens.
- 01
Find where the hours actually go
We shadow the work rather than reading the process map. The gap between the two is usually where the opportunity is, and it is never in the deck.
- 02
Score every candidate
Hours saved, data readiness, and blast radius when it is wrong. A process that scores well on the first two and badly on the third belongs behind a human, not behind an API call.
- 03
Build, buy, or leave alone
For each candidate, a recommendation with the vendor comparison behind it. Buying an off-the-shelf tool is frequently the right call and we will say so, including when it means a smaller engagement for us.
- 04
A roadmap that starts small
Sequenced so the cheapest useful pilot goes first and either earns the next phase or stops the programme early. Plus the data and governance work that has to happen before anything touches a model.
What you get
- Process audit: every candidate scored on hours saved, data readiness, and blast radius
- Build-versus-buy call on each one, with the vendor comparison behind it
- A data and governance readiness check before anything touches a model
- A phased roadmap with the cheapest useful pilot first
Built with
- OpenAI
- Anthropic
- Evaluation
- RAG
- Governance
The outcome
A ranked shortlist of what to automate, with the cost of being wrong priced in.
An honest audit of where AI would pay for itself in your business, and where it would just be expensive.
Get a free consultationScope and a fixed price before anything is committed. No obligation to proceed.
No dark periods. No surprise invoices.
A structured engagement from the first call to launch, so you always know what is happening and what it costs.
Week 1 · Discovery
Scope & fixed price
Then, every week after
A working demo.
The ones people actually ask.
Is this just a report?
It is a ranked shortlist with numbers attached and a recommendation per item, which is a report, but one you can act on the week it lands. If you want the first item built, that is a separate engagement quoted separately.
Do you only recommend things you can build?
No. A fair chunk of what comes out of these is 'buy this tool' or 'fix this spreadsheet first'. A consultant whose every recommendation happens to be their own service is a salesperson with a slide deck.
What about our data privacy and compliance?
That is part of the readiness check, not a footnote to it. Where data cannot leave your environment, the recommendation reflects that, whether through self-hosted models, redaction, or simply not automating that process.
How long does it take?
Typically three to four weeks for a mid-sized business. Longer where the work spans several departments, because the value is in seeing the handoffs between them.
Often bought alongside
Digital Transformation
Getting a business off paper, spreadsheets and eight disconnected tools, one process at a time.
AI Software Development
Products with a model at the centre of them, built with the evaluation and guardrails that keep them shippable.
Custom App Development
Internal software for the work your business actually does: portals, back offices, and the operations tools no vendor sells.
All services · Working on your own career instead? Career services for engineers
What is the most expensive thing your team still does by hand?
Tell us, and we'll tell you honestly whether software can fix it, and roughly what it would cost. No pitch deck.