Modernise the code you actually have. Not the one in the documentation.
Legacy modernisation usually starts with documents nobody trusts. We start with the code. ADIS agents index your repositories and build a model of the system as it stands; SpurTree engineers use it to assess impact, prioritise technical debt, plan refactoring and make changes through reviewed pull requests. Every finding is anchored to the current codebase, and every fix keeps its history.
You're in the right place if…
- Teams afraid to touch parts of a codebase because nobody fully understands them
- Organisations whose documentation no longer matches what is running
- Engineering leaders who need to show the business where technical debt sits and what it costs to fix
- Businesses that have lost the engineers who built the original system
What gets in the way.
Nobody knows what the code does
The people who built it have moved on, and the docs describe the system that was planned, not the one you run.
Debt argued about, not triaged
The loudest engineer wins, and the same issues are rediscovered every few months.
Changes with unknown blast radius
Without a clear map of dependencies, every refactor risks breaking something nobody expected.
No record of what was fixed
When debt does get paid down, the audit trail of what changed and why goes with it.
AI Code Modernisation, end to end.
Codebase analysis
Discovery agents index each repository and map its structure and conventions, giving us a model of the real system to interrogate from day one.
Impact assessment
Proposed changes are scoped against the actual code — files, interfaces and components affected — before work starts.
Technical debt register
Debt is surfaced, categorised and prioritised across every connected repository, so the worst of it is obvious.
Refactoring through reviewed changes
Impact and coder agents prepare refactoring changes; our engineers approve the plan and review every pull request.
Security findings in context
OpenGrep, SonarQube and CodeQL findings are anchored to the codebase as it stands, with fingerprinting so only new issues resurface.
History kept intact
Resolved findings are batch-cleared without losing the record of what was fixed and why.
A delivery approach you can plan around.
Index
We connect your repositories and build a current model of the system from the code itself.
Assess
We surface debt, security findings and risky dependencies, and assess the impact of the changes you need.
Prioritise
We agree with you which debt and modernisation work matters most to the business, triaged by impact.
Refactor
We deliver changes through approved plans and reviewed pull requests, keeping the register and audit trail current.
Ways to work with us.
Modernisation assessment
A scoped look at your codebase to map debt, risk and the order in which to tackle it.
Maintenance POD
A dedicated team working through debt, security findings and refactoring on an ongoing basis.
Technologies we work with
- ADIS
- OpenGrep
- SonarQube
- CodeQL
AI Code Modernisation: common questions.
Do we need up-to-date documentation to start?
No. We derive the description of the system from the code itself, which is often more reliable than existing documentation.
How is this different from application modernisation?
Application modernisation covers re-platforming and re-architecture decisions. AI code modernisation focuses on understanding, refactoring and paying down debt in the code, using ADIS agents. Many engagements combine both.
Will refactoring be done without our oversight?
No. Every change has an approved plan before work starts and a reviewed pull request before it merges, and your team can sit on any approval gate.
How long does an assessment take?
It depends on the size and number of repositories. We'll scope it with you once we understand your application landscape.
Let's talk about your ai code modernisation needs.
A modernisation lead will reply within one business day to scope the assessment.