Code and Dependency Analysis
AI-assisted reading of legacy code, schemas and batch jobs to map modules, dependencies and the business rules buried inside them, far faster than tracing everything by hand.
Legacy System Modernization Services
Replace the system that runs your business, while it keeps running
What is Legacy Modernization?
The system is a decade or two old. It works, it holds years of data and business logic, and it is increasingly expensive to change - the original developers have moved on, the framework no longer receives security updates, and every modification risks breaking something nobody fully understands. Meanwhile a full rewrite is quoted in years and carries the real possibility of delivering something worse.
There is a middle path, and it is almost always the right one. Modernise in slices: extract one capability at a time behind a stable interface, run old and new side by side, and migrate traffic gradually once the replacement is proven. Operations continue throughout, each step is individually reversible, and value arrives steadily rather than arriving all at once at the end - if it arrives at all.
Incremental Migration
Zero Downtime
AI-Ready Architecture
What We Deliver
Assessment and Roadmap
Map the existing system - dependencies, data flows, integration points and the business logic buried in code. Produces a prioritised plan sequenced by risk and value rather than by what is technically interesting.
Incremental Re-architecture
Extract capabilities one at a time behind an API layer while the original system continues serving everything else. Each slice is independently testable and reversible, which is what keeps the risk manageable.
Database Migration
Move from legacy or proprietary databases to PostgreSQL, MySQL or managed cloud equivalents, with schema redesign, data cleansing and reconciliation. Dual-write periods let you verify correctness before committing.
API Enablement
Wrap existing functionality in documented REST or GraphQL APIs so new applications can integrate without touching legacy internals. Frequently the highest-value first step, and far cheaper than replacement.
AI Enablement Layer
Prepare legacy data and workflows for AI use without forcing a full rebuild. We expose clean APIs, structure historical data, and add guardrails so copilots, search and automation can work safely with older systems.
Cloud Migration
Rehost, replatform or refactor onto AWS, Azure or Google Cloud depending on what each component justifies. Not everything needs refactoring; some workloads are better simply moved and left alone.
UI Modernisation
Replace dated interfaces with responsive React or Vue front-ends talking to the new API layer. Often the change users notice first, and the one that makes the rest of the programme visible to the business.
How AI Speeds Up Modernization
We use AI on the slow, repetitive groundwork of a modernization programme. Engineers review everything it produces and remain responsible for every decision and every line that ships.
Documentation Generation
Draft system documentation, data dictionaries and flow descriptions from the code and database structure, then have the people who know the business confirm them.
Characterization Tests
Generate tests that capture how the system behaves today, so every migrated slice can be proven to behave the same before traffic moves to it.
Code Translation Assistance
Draft conversions of well-bounded modules to a modern language or framework. Each one is reviewed, tested and refined by an engineer before it is accepted.
Data Mapping and Cleansing
Suggest field mappings between old and new schemas, and flag duplicates and inconsistencies, with automated reconciliation to confirm nothing was lost.
Change Impact Analysis
Trace which reports, integrations and downstream modules a change touches, so risk is understood before a slice is cut over rather than discovered afterwards.
Making a Legacy System AI-Ready
You do not have to finish modernizing before you can use AI. Each step adds a capability on its own, and each can go live without waiting for the next.
-
Expose
Wrap the legacy functions you need in documented APIs and events, so new tools can use them without touching fragile internals.
-
Structure
Clean, label and organise historical data so it can be searched, analysed and trusted, without moving it out of the system that owns it.
-
Ground
Index documents and records for permission-aware retrieval, so AI answers come from your own data and cite where they came from.
-
Assist
Add copilots, natural-language search and summaries inside the screens your people already use, with the old system still doing the work underneath.
-
Automate
Introduce AI-driven workflows and agents for repetitive tasks, behind approvals and audit logs, with a person reviewing anything consequential.
Problems This Solves
“We cannot afford downtime”
Incremental migration with parallel running means the old system stays available until the replacement is proven.
“Nobody knows how it works any more”
Code and data analysis reconstructs the business rules, which are documented as part of the assessment.
“We want AI but our system is too old”
An AI enablement layer lets modern assistants, search and automation use legacy data through controlled APIs instead of touching fragile internals directly.
“A rewrite was quoted in years”
Slicing the work delivers usable improvements in weeks and lets you stop or re-prioritise at any point.
“The technology is unsupported”
Unsupported frameworks are a security exposure. Prioritising the components with real risk addresses that first.
How We Work
01
Discovery
Analyse code, data and integrations; document what the system actually does.
02
Prioritise
Sequence by business risk, cost of change and value released.
03
Migrate in Slices
Extract, test and cut over one capability at a time.
04
Decommission
Retire legacy components once traffic has fully moved and been verified.
Talk to us and
get your project moving!
We will respond to you within 24 hours.
You'll be talking to product and tech experts.
NDA available upon request.