We know your industry.

What makes it hard

Decades of banking and payment systems piled on top of each other, under rules that treat every change as a risk. Upgrades stall for two reasons. Getting it wrong costs far more than getting it right saves, and nobody can tell you what depends on what.

What we do

• Replacing core systems piece by piece, never all at once
• Instant payments and open banking APIs
• Automating regulatory reports and tracking where figures came from
• Fraud and money laundering detection built on clean data
• Making systems resilient, and testing recovery properly
• Moving workloads to cloud where the rules allow it
• Round the clock support for the systems that cannot stop

Where a first project usually starts

Most banking work starts with a dependency map, because that is usually what is missing. We spend three to four weeks tracing what actually calls what, then rank the changes by risk rather than by how easy they look. You get a written plan you can take to your risk committee before any code is touched.

Questions we get asked

What makes it hard

Clinical, admin and research systems that were never built to talk to each other, holding the most sensitive data there is. Every decision about connecting them is three decisions at once: a technical one, a patient safety one and a privacy one.

What we do

• Getting clinical systems to share data using HL7 and FHIR
• Managing consent and building privacy in from the start
• AI that helps clinicians decide, with a full audit trail
• Research data platforms that strip out identifying details
• Joining up appointment, billing and records systems
• Secure access for staff who move between sites and devices
• Keeping services running during upgrades, with no downtime

Where a first project usually starts

We normally begin with one real handover between two teams, end to end, rather than a platform wide programme. It is small enough to finish in a few weeks and it surfaces every consent, safety and mapping question you will hit later. What we learn there sets the shape of everything after it.

Questions we get asked

What makes it hard

A big sale can bring in a large slice of the year’s revenue in a few hours. Meanwhile stock, pricing and delivery data sit in separate systems that disagree with each other, and every disagreement turns into a complaint.

What we do

• Building and improving online stores
• One source of truth for stock, prices and orders
• Personalisation and forecasting demand
• Getting ready for peak days by testing what breaks first
• Joining up shops, warehouses and online
• Payment and checkout work, including card data handling
• Support cover that scales up for your busiest weeks

Where a first project usually starts

For retail we usually start with a peak readiness review, because it has a deadline attached and the findings pay for themselves. We model your busiest hour, push the system until something gives, and hand you a ranked list of what to fix and what it will cost if you do not.

Questions we get asked

What makes it hard

Machines built to last twenty years now have to send data to the cloud, and the supply chain wants visibility those machines were never designed to give. The real difficulty is connecting them without opening a way in to the factory floor.

What we do

• Getting data from the factory floor up to the cloud
• Predicting failures before they happen, without crying wolf
• Joining factory and office networks safely, kept separate
• Seeing across your supply chain and planning demand
• Connecting and updating ERP and production systems
• Tracking assets and scheduling maintenance around production
• Reporting that plant managers and head office both trust

Where a first project usually starts

We start on one line or one site, never the whole plant. Getting real data off a handful of machines and into a report someone actually uses proves the connection is safe and the numbers are trusted. Once that holds for a few weeks, rolling it out is a known quantity.

Questions we get asked

What makes it hard

Learning platforms succeed or fail on whether people stay and whether it works. But most only track who finished. Schools and EdTech companies alike struggle to link what a learner did to whether they actually got better, or got hired.

What we do

• Building learning platforms that handle real numbers of users
• AI guided learning paths and tests that adapt
• Job readiness scoring and matching skills to roles
• Connecting student records systems and reporting across them
• Learner privacy, consent and safeguarding
• Handling enrolment surges at the start of every term
• Reporting that shows whether teaching actually worked

Where a first project usually starts

We usually begin by instrumenting what you already have. Most platforms record who finished and little else, so before building anything new we make the existing one tell you where learners stall and why. That evidence tends to change what people thought they needed to build.

Questions we get asked

What makes it hard

Old systems that cannot simply be switched off, accessibility and language duties that are not optional, and a buying process that rewards paperwork over working software. Projects have to show something useful in public before they are finished.

What we do

• Designing and building services the public actually uses
• Updating old systems without the service ever going down
• Cloud setups that keep data inside the country
• Meeting accessibility rules, in more than one language
• Getting different departments’ systems to share data
• Publishing service performance openly
• Handing over cleanly so your own team can run it

Where a first project usually starts

We start with the smallest complete service we can put in front of real users, not with a platform. Something the public can finish end to end, however narrow. It proves the approach in public, gives you something to show, and makes the next round of funding a much easier conversation.

Questions we get asked