We ship agents, not just code
We already run AI agents in production for drawing review, recruitment screening, quotations, email and e-commerce. The same engineering goes into your build.
AI-driven delivery
AI-driven software development
AI agents do the volume work across the whole delivery cycle. Senior engineers own the architecture and the result. The repository, the agents and the documentation stay yours.
- 15+
- Years in enterprise delivery
- 68
- Delivered case studies
- 100%
- Commits reviewed by a senior engineer
- On-prem
- Deployment where data cannot leave
Why now
Traditional delivery cannot keep up
AI changed how fast software can be specified, written and tested. Teams still doing all of it by hand are paying for time instead of outcomes.
- Hand-written specs and boilerplate burn senior time that belongs in architecture.
- Most AI pilots never reach production, because nobody agreed what success meant.
- Legacy systems in regulated industries block change the longest, and that is where the cost sits.
- Without a KPI agreed up front, nobody can tell whether the AI actually helped.
How we deliver
Four phases, AI in every one of them
The same four phases run on every project, whether it is a single agent or a core platform. What changes is the scale, not the discipline.
- 01
Discovery and AI audit
We map the process and pick the target with the clearest return. The KPI is written down before the first line of code.
- We walk your process and the data behind it
- We pick the task that pays back soonest
- We write down a measurable target and a date
- 02
Architecture and specs
AI drafts the specification, the test cases and the scaffolding. Architecture and security decisions are made by people.
- An architect designs the data model and integration boundaries
- AI produces the first draft of specs and user stories
- Security and access are handled now, not at the end
- 03
Build under review
Agents write and test the code. A senior engineer reviews every commit before it reaches your repository.
- Automated tests and coverage checks on every change
- Security gates and CI/CD from day one
- Code review is done by a person, not a model
- 04
Operate and hand over
We set up monitoring, write the runbook and train your team. Then we measure the KPI from phase one.
- Monitoring and an incident procedure
- Training your team on the agents we built
- The target measured against the number from phase one
Division of labour
What AI does, and what stays human
The honest answer to the question we get most often. How much of this is the machine?
AI agents handle
- First drafts of specifications and user stories
- Boilerplate, scaffolding and repetitive refactors
- Test generation and coverage gap analysis
- A first pass over the code for style, typing and obvious defects
- Documentation and runbooks kept in step with the code
People stay responsible for
- Architecture, the data model and integration boundaries
- Security, access control and anything touching regulated data
- Reviewing every commit that lands in your repository
- Trade-offs where business context matters more than code
- Accountability for the delivered result
Why us
We build it, hand it over and train you
Most teams stop at delivery. We leave you able to run it without us.
On-premise when the data cannot leave
Banks, insurers and the public sector often cannot send data to a third-party API. Where that is the requirement, we deploy inside your own infrastructure.
We train your people
Our AI courses are taught on your own documents and tools, so the agents we hand over actually get used after we leave.
Fifteen years in regulated industries
68 delivered enterprise case studies across banking, telco, insurance and payment systems. The AI is new. The delivery discipline is not.
FAQ
The questions clients actually ask
How much of the code is written by AI?
It depends on the project: most of the boilerplate and tests, a minority of the core business logic. A different number matters more. Every commit is reviewed by a senior engineer before it reaches your repository.
Are our code and data safe?
Your code is not used to train public models. For regulated work we deploy inside your own infrastructure, so the data never leaves your network. We agree the data boundary in writing before the project starts.
What happens when the AI gets it wrong?
That is what phase three is for. Agents write the code, tests run automatically and a senior engineer reviews it. That review is not optional and is not done by a model.
Will we depend on you afterwards?
No. The repository, the agents, the CI/CD pipeline and the documentation are yours. We also train your team so they can run it.
How do you prove it worked?
We write the KPI down in phase one, before any code exists. In phase four we measure against it. If the number did not move, we say so.
Let us agree the KPI before we build
Tell us which process costs you the most. We will tell you whether AI-driven delivery is the right answer and what it would take.