Reality first
We begin with your constraints, not our preferences. Legacy systems, lean teams, compliance regimes, fixed budgets: these aren't obstacles to the plan, they're the material it's built from.
AI Consulting
Strategy Grounded in Reality and Foresight
Clear guidance that survives the next wave.
What we advise on
The decisions we are brought in to make
Readiness
A bill of health for the systems, the data and the team: what is a green light, what is amber, and the groundwork that clears the rest.
AI strategy
Where AI belongs in the business, what it is meant to change, and what that is worth — decided with numbers rather than adjectives.
Use-case prioritization
Candidates mapped across the work, ranked on feasibility and return, each one leaving the room with an owner attached.
Build vs buy
Model, platform and tooling evaluated against the job in front of them, with the real cost of each over three years.
Roadmap and sequencing
What gets built, in what order, and what each stage returns before the next one starts.
Operating model
Who owns AI once it is live: roles, funding, delivery cadence, and the tracking that keeps value visible.
Governance and compliance
Risk controls, acceptable use and oversight, mapped to the EU AI Act, GDPR and whichever regime your sector answers to.
Fractional AI leadership
A head of AI in the room for as long as it takes, until the decision-making sits with someone permanent.
Where clients typically come to us
For decisions that are too expensive to guess
You might be curious, committed, or burned once already.
Wherever you're starting from, the work is the same: evidence first, decision second, plan third.
Where do we even start?
Good instinct. Asking first costs less than buying first. We map where your time and errors concentrate and name the one place to begin.
The budget is approved. The plan isn't.
Funded but unplanned is the most dangerous phase of an AI initiative. We sequence it before the first invoice: what first, what deferred, and why.
The pilot that never quite shipped
Great demo, no production. We find the gap, name the blocker, and get it shipped or honestly closed.
Is our tech lead's plan the right plan?
A senior second opinion on architecture, models, and sequencing: confirm what's right, catch what isn't, fix it while it's free.
Too many vendors, no trusted filter
We don't sell licenses, take vendor margin, or have a platform to push. That makes us a rarer filter than you'd think: the only side we take in the evaluation is yours.
Reality + foresight in practice
Practical today, portable tomorrow
Foresight built in
When the next generation ships, our clients upgrade in days, not rebuilds. That speed comes from decisions made before the first line of infrastructure, not after.
Test the method
The audition is structured deliberately: narrow scope, defined deliverable, fixed price. Whatever the outcome, you'll know more about your own operation than before.
Start here
A quick conversation.
What's realistic now, what's premature, and what an audit would answer.
Our guarantee
No uplift =
no invoice
Baselines agreed before we start, metrics agreed before we bill. If the numbers we committed to don't move, the invoice doesn't get sent.
Baselines before invoices
Kickoff includes a metrics session: we pick the numbers that matter, record where they stand, and set thresholds for what counts as moved.
Scope you can verify
You'll never see a scope line you can't test. If we can't show you how an item would be verified, the item waits until we can.
Same guarantee on the build
From the roadmap through the build to the training handover, every phase answers to the metrics agreed before any of it began.
The process
From call to decision
01
Discovery
A free call with one job: we listen to your situation and say plainly whether an audit would pay for itself.
02
Audit
A sprint inside your real numbers and systems. Everything after this is grounded in what we find here.
03
Roadmap
A phased plan with priorities, costs, returns. Decision-ready whether you build with us or elsewhere.
FAQ
Answers in advance
What if the audit says we're not ready for AI?
Then we've probably saved you the most expensive sentence in AI: "we'll fix the foundations later." You get the specific blockers, the groundwork that clears them, and an honest window for when a second look makes sense.
How do you tell the difference between a demo that impresses and a system that survives production?
We ask the boring questions early: what happens at 10x volume, who owns it at 2am, what it costs per thousand runs, how failures get caught. Demos are built to avoid those questions; production systems are built around them.
What would make you tell us our data isn't good enough?
Concrete flags: data scattered across silos with no shared identifiers, labels that don't exist for the outcome you want to predict, history too short to establish baselines, or quality that drifts because nobody owns it upstream. We check all four in the first week.
Do you work inside our compliance perimeter, or does documentation leave it?
Compliance regime first, method second. Whether that's GDPR, sector regulation, or internal classification, the engagement plan starts from your rules. The roadmap even accounts for them. Advice isn't portable if it ignores the law of the land it runs in.
How do you keep the running cost from eating the uplift?
We treat operating cost as part of the deliverable. Targets for cost per transaction are written into the roadmap and monitored like any other metric; the modular design means a cheaper solution is a straightforward swap, not a rebuild.
AI transformation