The AI part is new.The discipline &experience aren't.
For twenty five years we have built the systems large organisations actually run their day-to-day on: intraday exposure reporting at Anglo American, bond research at the London Stock Exchange, master data and a reporting warehouse at SPAR. We are now applying the same engineering discipline and experience to AI enhanced systems development.
Shipped for
Built faster, held to the same bar
AI has changed how quickly a system can be built. It has not changed what makes one correct, maintainable, or safe to put in front of your customers.
Generated code is a draft. Someone still has to be accountable for what ships.
Agents do the typing, engineers own the design
The architecture, the data model and the failure modes are still decided by people who have run production systems for over two decades. What AI removes is the fortnight of boilerplate between making that decision and having a working application to argue with, not the decision itself.
Context is the actual work
A coding agent is only as useful as what it can see: your schema, your conventions, your existing services, and the reason the ticket exists at all. We spend the setup time assembling that context and curating the skills an agent reuses, because it is the whole difference between plausible code and code that fits your system.
Review that assumes the code is wrong
Everything generated goes through the same tests, the same pipeline and the same human review as anything hand-written, and with rather more scepticism, a confident wrong answer looks exactly like a right one. The speed comes from the drafting. It never comes from skipping the checks.
You get a codebase, not a dependency on us
Delivered quickly is worth nothing if what lands is unmaintainable. Structure, naming, tests and documentation are held to the standard your own team would have to work in, whether or not they use the same tools we did.
Systems in production,not slideware
Reference 001
Anglo AmericanLondon
Live intraday exposure reporting for the trading desk
Reference 002
London Stock ExchangeLondon
US municipal bonds information and research platform
Reference 003
Clifford ChanceLondon
Partner remuneration across multiple tax regions
Reference 004
The SPAR GroupDurban
Store and vendor master data, plus a reporting data lake migrated to Azure
Reference 005
UniperGermany
Power purchase agreement valuation, visualising large time-series datasets
Reference 006
Chelsea Football ClubLondon
Live player analysis, ingesting streaming data from the pitch
Reference 007
Fluenty IT — LisaSouth Africa
Lead-to-lease platform: marketing automation, deal management and portfolio analytics
Reference 008
Fluenty IT — TTTF PayrollNigeria
Multi-tenant Nigerian payroll platform: pluggable tax rulesets, pay-run processing and statutory reporting
Reference 009
DigitalTwinSouth Africa
Live asset tracking over Bluetooth low-energy beacons
Reference 010
SMEasySouth Africa
Online accounting package for small business
Reference 011
CashRewardsAustralia
Customer coupon management
DreamDesignDevelopDeliver
Four words we have had on the door for years. They are not a methodology diagram — they are the order in which we do the work.
Dream
We start with the outcome, not the ticket list. What would this business be able to do that it cannot do today, and is software actually the thing standing in the way?
Design
Architecture decided before the first sprint: where state lives, what fails independently, what the system does at ten times the load. The expensive mistakes are all made here.
Develop
Small increments behind tests and a build pipeline, reviewed by people who have run production. You get working software to argue with rather than a status report.
Deliver
Into your environment on a short, regular interval, with the handover written as we go. Nobody should wait six months to discover a project is behind.
What we work in
Deep in the Microsoft stack, comfortable outside it. We pick the boring option unless there is a reason not to.
Cloud & data
- Microsoft Azure
- Azure DevOps
- Azure Data Factory
- Azure Cosmos DB
- SQL Server
- PostgreSQL
- MongoDB
- AWS Lambda
- Docker
Application
- C# / .NET
- ASP.NET Core
- TypeScript
- Angular
- React
- Node.js
- SignalR
- GraphQL
- React Native
AI systems
- Claude Code
- OpenAI Codex
- Grok
- Context engineering
- Skills curation
- Subagent orchestration
- MCP tool integration
- Spec-driven delivery
- Automated code review
Tell us whatisn't working yet
Most conversations start with a system that has outgrown itself, or an AI pilot that works in a demo and nowhere else. Send us the shape of the problem and we will tell you honestly whether we are the right people for it.