AVENIR TECHNOLOGY PARTNERS
Approach
From the boardroom question to the system your people use every day: strategy, operating model, change and engineering, delivered as one agile method with a clear decision at every stage.
What we do
Most AI programmes stall between the strategy and the software. We work across that gap: the same team frames the value, shapes the organisation around it, and builds and runs what delivers it.
An AI strategy set with the board: where AI creates value in your business, what to build, buy or partner on, and the investment case behind each move.
Your technology operating model aligned to AI: the teams, roles, decision rights and governance it needs, and the change management that makes it stick. Governance is designed against the EU AI Act, ISO/IEC 42001 and the NIST AI Risk Management Framework.
The data AI depends on, made ready for it: sources found and assessed, quality and lineage fixed where the data is made, and the platforms and access controls that let models use it safely.
Production AI that does real work: agentic workflows, assistants grounded in your own knowledge, and document, vision and voice intelligence, each measured before it ships.
AI connected to the systems that run the business, through clean, secure interfaces, so it reads the right data and its actions land where the work is done.
Enterprise platforms selected, configured and rolled out with their AI capabilities switched on deliberately, measured against your own processes rather than the vendor demo.
Systems you depend on, understood, re-platformed and extended. AI-assisted code analysis maps what the old system really does, each part is retired, re-platformed or rebuilt on its merits, and new capability takes over one slice at a time.
Web, desktop and mobile products built to a native standard, with AI designed in from the first sketch, from design system to cloud and edge back end.
Fixed-scope proofs that end in a measured decision, and pilots in live operations with real users, each with its success measure and its stop condition agreed up front.
Systems kept accurate, safe and affordable after launch: monitoring, evaluation, cost control and model updates, or a clean handover to your own team.
Your own people confident with AI, from the board to the engineers: practical training in using AI well in every role, and hands-on full-stack programmes that take technical teams from models and retrieval to agents, evaluation and production, built on your own tools and data.
The people to carry the work on: whole technology teams hired and stood up, executive search for CTOs, CIOs and heads of data and AI, and interim leaders from our network to hold a role while the permanent hire is found. Every candidate is assessed by people who do the work.
How we work
One method for every engagement, whether it is a proof of concept, a new product or intelligence added to systems you already run. It pairs a strategist’s discipline about value with an engineer’s discipline about delivery.
01Frame
We find where AI changes the numbers that matter to you, and agree how we will know it has.
A ranked set of opportunities, a measured baseline, and one chosen to prove.
02Ground
We survey your data, systems, people and constraints as they really are, not as the diagrams say.
A map of data and systems, a risk register, the first architecture and a prioritised backlog.
03Prove
We build the thinnest slice that does the real job, and measure it on cases it has never seen.
Accuracy, speed and cost against the baseline, and a decision: go, change course or stop.
04Engineer
We turn the proof into a system that is secure, observable and safe to change, increment by increment.
A release that passes its evaluations, its security review and its load tests.
05Land
A system nobody uses has no value, so we put it into the work alongside the people who do it.
The system in daily use, with adoption and value tracked against the baseline.
06Run
Real use is the final test, so we watch it closely and improve on what it shows.
A system with an owner, a live dashboard and a measured backlog of improvements.
Agile by design
Scope is prioritised with you, so the deadline holds. The six stages follow DSDM’s project lifecycle, made for AI, and Scrum runs the work inside every timebox.
Ways to start
Every engagement uses the same six stages. What changes is where the weight falls, and how much of the road you need us for.
Proof of concept
Frame, ground and prove, answered with numbers in weeks. Nothing is thrown away: the test set and the measures carry straight into the build.
Pilot
A proven capability put into live operations with a defined group of users and measured against the baseline. It ends in a decision to scale or stop, never in a pilot that runs on.
Greenfield
All six stages, with design and engineering leading. The AI is part of the product’s structure, not something added at the end.
Legacy modernisation
Ground goes deepest. The new capability runs beside the old system on real traffic, is proven there, then takes over one slice at a time.
What we hold to
Methods change as the technology does. These do not.