AI strategy & consulting

Find where AI
actually pays off

Most AI roadmaps are a list of things that sound impressive. We look at your business, your data and your constraints, and come back with a short list of things worth doing and an honest account of what they cost.

Rated 4.9 on Clutch across 38 reviews


2-4weeks for a full
assessment and roadmap
3-5opportunities scored
on return and effort
100%of estimates include
the cost to run, not just build
0obligation to build
any of it with us

What the engagement covers

Six questions, answered with evidence rather than opinion.

Opportunity mapping

Where in your operation the repetitive, judgement-light work actually sits, sized by how much of it there is.

Data readiness

Whether the data needed even exists, where it lives, how clean it is and what it would take to make it usable.

Cost modelling

What each option costs to build and, more importantly, to run per month at your real volumes.

Build, buy or wait

Plenty of what teams ask us to build is already a feature in a tool they own. We will tell you when that is the case.

Risk and compliance

Where your sector, your contracts and your data policy constrain the options, established before anyone starts building.

A sequenced roadmap

What to do first, what it unlocks and what should wait, with the reasoning written down so you can argue with it.

How it works

We are as useful when the answer is that AI is not the constraint. That conclusion has saved clients more than most of our builds have.

Your side

What we look at

  • How the work is done today
  • Your systems and data
  • Team skills and capacity
  • Sector and contract constraints
What we do

The assessment

  • Interviews with the people doing the work
  • Data readiness review
  • Opportunities scored on return and effort
  • Cost to build and cost to run
  • Sequenced roadmap
You get

The output

  • A written assessment
  • A costed shortlist
  • A roadmap you own
  • No obligation to build with us

How the engagement runs

Two to four weeks, mostly spent talking to the people who do the work.

1

Frame the question

A first session to establish what you are actually trying to improve. Often it is a business problem that has been handed over as an AI request.

2

Talk to the team

Interviews with the people doing the work day to day. They know where the time goes better than any process document does.

3

Check the data

Whether the data needed exists, is accessible and is good enough. This is where most promising ideas turn out to be premature.

4

Score and cost

Each opportunity sized on return, effort, risk and monthly running cost, so the comparison is like for like.

5

Hand over

A written assessment and a roadmap presented to your team, yours to execute with us, with someone else, or internally.

Built by a team that ships

The AI layer is new. The engineering underneath it is not. These are products we designed, built and still maintain.

Planable Omniconvert Wolfpack Digital OPEN social CANGO Mobility Zerotak TaskManager Bookster Life in Codes HCT Envision Tickbird
VerityPanel Market research platform

Data collection at scale

A digital market study and data-collection platform built for fast, reliable research at scale.

Product development · Frontend
AccessGO Accessibility platform

Usable for everyone

AccessGO is a digital accessibility platform that helps businesses make their products usable for everyone.

Web development · Accessibility
Cybersecurity Cybersecurity

A trustworthy presence

Zerotak helps organisations strengthen their security posture with clear, actionable protection.

Web development · Branding

What we look at

The assessment follows the same structure every time, so the conclusions can be compared and challenged.

Interviews, watching the work, and a walk through the systems

We spend the time with the people doing the work rather than with the process documentation, because the two rarely match.

Is the data there, is it any good, and can you get at it?

The most common finding is that the data exists but nobody can get at it. That is a smaller and cheaper problem than it sounds.

What it costs to build, to run and to maintain

Running cost is what surprises teams. We model it at your real volumes, including the cases where usage grows faster than value.

Sector rules, contracts and your own data policy

Established at the start. Discovering a constraint after the build is the most expensive way to learn about it.

Sequenced, costed, and yours to keep

Written so you can hand it to another supplier. If we have done the assessment well, that should still be a fair option.


“We appreciated all the work and open dialogue we had around the ideation and the execution of the project.”

Crina Fratean · Comm and Marketing Lead, Salt and Pepper Verified review on Clutch

Got an idea? Let’s make it real.

Tell us the short version

This could be the first step towards a new and successful collaboration. A one-line idea and a finished spec are both fine — tell us the problem, the deadline you’re working to and what’s in your way.

We reply within one working day.

Prefer another way to talk?

Frequently asked questions

A written assessment, a shortlist of opportunities scored on return and effort, a cost model covering both building and running, and a sequenced roadmap. It is written so you could hand it to another supplier.

No, and the assessment is priced so that walking away is a reasonable outcome. If the roadmap is any good it should stand on its own regardless of who executes it.

Then we say so, and explain what the real constraint is instead. That conclusion has saved clients more money than most of our builds have made them.

Two to four weeks, most of it spent talking to the people who actually do the work rather than reading process documentation.

The people doing the work day to day, someone who knows where the data lives, and whoever can speak to contractual and regulatory constraints. Roughly a handful of hours each.

At your real volumes, including the case where usage grows faster than value. Running cost is what surprises teams after launch, so it is modelled alongside the build estimate rather than after it.