Data and AI consultancy
AI that fits your business, not the other way around.
Your business is unlike any other. We build AI solutions tailored to your proprietary data and ways of working, giving you an advantage your competitors cannot replicate.
The clients who trusted us with the hard part.
Who is this built for?
Businesses with real data, and no AI team of their own.
- You know AI could change something in your business, and you cannot say what, or where to start.
- You have years of data across systems that were never designed to talk to each other.
- You have seen demos. You have not seen anything reach production.
What are we built to solve?
The impact we deliver.
Artificial intelligence offers endless possibilities. The real challenge is moving from believing AI could add value, to understanding exactly where it will, what form it should take, and how to get there.
Problems we can solve
“We know AI could help us, but with so many tools and options, we do not even know where to begin.”
Usually raised by leadership, marketing or technology
We start with what is slow, expensive or uncertain, rank where AI would make a real difference, and tell you which ideas to leave alone. You leave with a shortlist, not a technology roadmap.
“We have data everywhere, nothing talks to each other, and it is a headache to get a clear picture.”
Usually raised by technology, data or operations
Every team has some degree of data mess. We map the platforms that matter, define the shared attributes, and make your data usable before anything is built on it.
“We have years of customer data and still send the same offer to everyone.”
Usually raised by marketing, CRM or commercial
Practical systems that put the right offer in front of the right person, on your own history. Your team sees what is working and where revenue is moving.
“Our customers ask questions every day, but finding the right answer is still harder than it should be, for them and for us.”
Usually raised by digital, service or marketing
Conversational tools for customers and for staff, grounded in your own material, so the answer arrives in seconds rather than after a search.
“Our support team answers the same questions over and over, and it is eating up time we could spend helping customers with real issues.”
Usually raised by service, operations or customer experience
Support is one of the highest costs in any business. We deflect the repeated questions, escalate the rest with context, and build a knowledge base that keeps improving.
“We are buried in manual tasks every day. It feels like we never have time for the work that actually moves the business forward.”
Usually raised by operations, marketing or finance
Teams lose up to a quarter of the day to repetitive work. We find where the time goes, then automate the routine so your people are left with the parts that need judgement.
“We know we need automation, but building it on our current processes just feels like speeding up the mess.”
Usually raised by operations, technology or leadership
Automation layered onto a bad process makes it worse, faster. We rebuild the workflow first, then automate what is left.
What have we built?
Systems in production, not pilots.
Each started as a thin line inside the client's real constraints and grew. Each carries two figures: what changed, and where a person stayed in the loop.
AI A global commercial bank · Financial services
A data-driven optimisation programme across six Asian markets at 12:1 ROI
Digital Wilderness Destinations · Travel and leisure
Doubling conversion rate and cutting acquisition costs in half
Digital Capco · Professional services
Rebuilding a global digital platform from scratch, delivered in 120 days
Why does most of this not work?
Most AI work never reaches production. The model is almost never the reason.
There are six ways these projects die, and we have watched all of them. The way we work is built to avoid each one.
AI and cloud partners.
What do we actually sell?
Three outcomes. Five ways in.
Every engagement starts with a commercial outcome, agreed and measured before anything is built.
Revenue and growth
Who is likely to buy, what they want next, and when to reach out. And when they arrive, making sure they can find it. Your team makes the final call.
Revenue and growthEfficiency and cost to serve
Reading the same documents, answering the same questions, rebuilding the same report. Automated without removing the judgement, so your team gets the cases that need them.
Efficiency and scaleCapability and control
The hard part is not the first project, it is the second and third. Data sorted, governance your team will use, people trained to spot the next thing. You should need us less over time, not more.
Capability and governanceAnd how do we start?
Opportunity assessment
Time with your people and your data. A clear picture of where AI will make a difference, what it is worth, and where to start.
The first thin line
The simplest path from A to B that works inside today's constraints. One use case, end to end, in production. Value in weeks rather than months.
Build capability
More sources, more features, more capability, each added on top of something already running.
Enablement programme
The tools and training to run it in-house, with us alongside deciding what to improve next.
Retained partnership
Analysis, strategy and delivery against a shared roadmap.
What are we thinking about?
Practical, not theoretical.
Everything we publish has to be usable on Monday. Not another conversation about the art of the possible.
Field note
Start with a thin line, not a strategy
Why the narrowest working path beats an eighteen-month data programme, and how to scope one.
Report
The AI Layer
How AI is actually being used across travel and hospitality, and which claims stand up to scrutiny.
Podcast
Why 95% of pilots fail
F{AIL}s, with Mario and Steve. Most AI failures turn out to be human failures.
Where do I start?
Tell us what is slow, expensive or manual.
We will tell you honestly whether AI is the answer, what form it would take, and what a first build looks like. If the honest answer is not yet, we will say that too.
