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What we work on

Practice areas — the kinds of problems we solve.

A snapshot of the work we have been doing, organised by sector and capability. Detailed client case studies will follow as clients are happy to share more — the categories below describe the kinds of problems we have solved, not specific projects.

01 Property × Government — UK

Real-time portfolio sync between a property firm and HM Government

Built a real-time data interchange between a central London property management company and the UK government — keeping a sizeable property portfolio in sync between both systems end-to-end. SQL Server, .NET, web services. Designed to live in production, not to be re-built every two years.

.NET · SQL
Stack
Real-time
Two-way data sync
02 Energy — UK Nuclear

Real-time bid auction for UK nuclear site sales

Designed and built a secure real-time bid auction platform used to sell UK nuclear power sites. Web-based stack, secure document-sharing area, full audit trail — designed for high-stakes commercial transactions where every bid and every document mattered.

High-security
Auction + document platform
Real-time
Bid engine
03 Music — Multi-platform

One online music company, every screen that matters

Full-stack delivery for an online music company across web, iOS, Android, smart TV channels and connected devices. One product, one brand, the same care on every surface. Built and shipped end to end — the kind of multi-platform work most studios sub-contract out.

Web · iOS · Android · TV
Platforms shipped
Full-stack
In-house, end to end
04 AI in legacy systems

Bringing a legacy CRM bang up to date with AI agents

Integrated AI agents and AI-augmented working practices into a legacy CRM platform — drafting, retrieval, classification, smart routing — woven into the daily life of the team using the system. A tired CRM became something the team actually wants to open in the morning.

Integrated
AI in the working life
In place
No rip-and-replace
05 AI-augmented delivery

AI dev teams compressing the project lifecycle

We use AI engineering teams to change how projects actually run — automated research, automated test generation, scheduled AI dev teams doing continuous work alongside humans. Project lifecycles that used to take quarters now take weeks; the work itself stays serious.

Quarters → weeks
Lifecycle compression
Continuous
AI teams scheduled in
Sound familiar?

Something similar on your list?

If one of these looks like the shape of a problem you are wrestling with, we are happy to share more detail on how we have approached it. No NDA dance required.