Financial services software carries a burden most software does not: someone outside the business may need to understand how a decision was reached, months after it was made. That shapes everything, from how data is modelled to how a model's output is logged.
Our work in safety-critical and heavily regulated sectors is built on the same footing. RailGard AI scores fatigue risk against the expectations of the ORR, HSE and Network Rail, with every contributing factor recorded and explainable. ECS Group Command carries audit trails, role-based access and certification evidence across nine service areas. The regulator differs; the discipline does not.
Insights
Your data strategy is the AI project
Most AI work fails long before the model, in the reconciliation nobody wanted to do. No amount of model selection rescues it.
Read the articleExplainability is not something you add at the end
If a model contributes to a decision someone can challenge, the explanation has to come from the same machinery that produced the answer.
Read the articleWhy AI pilots stall before production
The gap between a convincing pilot and a system people use is not a modelling gap. It is the work nobody scoped.
Read the article
How we support changemakers in financial services
Set up and run teams
We work inside your teams rather than at arm's length from them, so decisions get made in the room and the knowledge stays with you after we go. That usually means a small senior group embedded alongside your people, not a large one managed from a distance.
Launch new products
We build to production standards from the first sprint: tested, observable, documented and deployable. In regulated work the thing that slows a launch is rarely the feature, it is being unable to show how it behaves, so we build that in rather than retrofitting it.
Discover new propositions and customer experiences
We work out what is worth building before anything gets built: research with the people who will use it, prototypes that can be tested cheaply, and honest commercial framing. We would rather talk you out of a product you do not need than bill you for it.
How we strengthen your team
On-demand access to the studio
Product design, engineering, cloud and AI in one team, available for the parts of a programme where you need depth rather than headcount. Twenty-five years of software delivery across the team behind it, and five spent putting AI into production.
Specialists when the work needs them
Data engineering, security, accessibility and model governance are brought in for the phases that call for them rather than carried on every project, so you pay for the expertise at the point it changes the outcome.
AI tools and human methods
We use AI to accelerate our own build and to make experimentation cheap enough to be honest about what works. What we ship is judged the same way as any other code: tested, reviewed and explainable.
Explainable by construction
Where a model contributes to a decision, each input is recorded as it stood at the time, alongside its contribution, the version of the logic that ran and what a person did with the result. We built scoring this way for safety-critical rail work, where a score has to be defensible line by line rather than taken on trust.
Audit trails and access that follow the role
Role-based access, audit logging and certification evidence assembled as the work happens rather than reconstructed afterwards. Built already across nine service areas, where an operative, a supervisor, an administrator and a client each see only their part of the platform.
Data strategy before models
The same customer or asset appearing three different ways across four systems is the usual starting point, and no model survives it. We reconcile the entities and fix collection at source first, because that is what decides whether anything built on top holds up in production.
White paper
AI governance in regulated environments
A practical guide to putting AI into production where decisions have to be justified: data strategy, model documentation, human oversight and the evidence a regulator will actually ask for.
Our work
Let’s talk
Have a problem you need to solve or an idea you want to explore? Let’s talk about how we can make it happen.