Services / Product Engineering
We modernize ageing applications, platforms and data in small, verifiable stages. AI speeds up understanding the old code and migrating it; engineers stay accountable for every change, and your operation keeps running throughout.
Incremental cutovers. Tested rollbacks. No big-bang rewrites by default.
The business case
Unsupported platforms, scarce skills, fragile releases and data trapped in old systems slow every change and widen security exposure. Modernization is worth it when it reduces those costs measurably, not because the technology is old.
A system the business depends on has become hard to change, secure, integrate or staff, and you need it improved without interrupting operations. Access to the code, data and people who use it is essential.
If the system is stable and only needs an integration point, an API layer may be enough. If the process itself is new, building a fresh web application can be simpler. The assessment tells us which.
What we modernize
These are example situations, not pre-packaged promises or claims about past results.
Rebuild or restructure the application your operation depends on, typically on unsupported frameworks or platforms, without stopping the work it carries.
Learn: does the new system run the same critical workflows with fewer incidents and less manual workaround?
Separate a tightly coupled codebase into well-bounded modules or services where independent change and deployment actually pay off, not as a default.
Learn: can teams change one area without coordinated releases across the whole system?
Move thick-client or server-room applications to a web interface and managed infrastructure, with access control, backups and monitoring designed in.
Learn: are availability, access and running costs measurably better than before?
Migrate schemas and historic records from legacy databases or file stores with mapping, reconciliation and a rollback point at every cutover.
Learn: do reconciliation counts and sampled records match before the old store is retired?
Wrap legacy systems in documented APIs so new products, partners and automation can use them without touching fragile internals.
Learn: can new consumers integrate through the API without legacy-specific knowledge?
Restructure data, interfaces and permissions so AI features can be added safely later: clean records, explicit access boundaries and traceable actions.
Learn: could an AI feature use this system's data and actions without bypassing its controls?
Choosing a strategy
We choose a strategy per capability, based on its value, risk and condition. Most programmes combine several.
Put an API or interface layer in front of the existing system.
Fits when the core is stable and the problem is access or integration.
Move to new infrastructure with limited code change.
Fits when the platform, not the application, is the risk.
Restructure code in place behind tests, module by module.
Fits when the logic is valuable and the codebase is recoverable.
Route functionality to a new system one capability at a time.
Fits when parts must change substantially but a big-bang cutover is too risky.
Write a new system against documented behaviour.
Fits when the existing code cannot be safely changed. Highest cost and risk; justify it first.
Decommission features nobody uses.
Often the cheapest modernization. Usage evidence decides, not opinion.
How the migration works
A routing layer sits in front of the legacy system. Capabilities move to new modules in stages, each passing a verification checkpoint, while the old system gradually goes quiet and is then retired.
Characterisation tests, a rollback per cutover, data reconciliation and observability. These are what make incremental change safe.
Unverified rewrites, migrating features nobody uses, and architecture changes that do not serve a stated goal.
AI in the modernization process
Language models are useful on legacy work because much of the effort is reading, explaining and translating code nobody fully remembers. We use them as accelerators inside a reviewed engineering process, never as an unattended rewrite.
Models summarise modules, trace call paths and draft explanations of business rules buried in old code. Engineers verify against the running system before anything becomes documentation of record.
AI-assisted analysis helps inventory dead code, duplicated logic, risky dependencies and undocumented integrations, producing a starting map that engineers confirm.
Models draft tests that pin down current behaviour, including the odd behaviour users rely on. Tests are reviewed and run against the legacy system so they describe reality, not intent.
For language or framework migrations, AI drafts translated code in small units. Every change goes through review, the characterisation suite and static analysis; output that cannot be verified is rewritten by hand.
Models propose field mappings, classify messy free-text records and flag anomalies. Proposed transformations are applied as versioned, reconciled scripts, with uncertain records routed for human review.
We do not send code or data to a model provider without your approval of the provider, region and retention terms. Self-hosted or approved private deployments are available where policy requires.
AI in the modernized system
Modernization is often the moment AI becomes practical: data is cleaner, interfaces are explicit and actions are permissioned. We add AI features only where they earn their latency, cost and review overhead.
Retrieval over approved, permission-filtered records so staff can find information that used to need a specialist or a report request.
Extract and validate data from forms, emails and documents into the new system, with confidence thresholds and review queues for low-certainty items.
Drafting, summarising and classification within existing screens, with users confirming consequential output and every AI action recorded on the audit trail.
Where automation is justified, agents act only through the same authenticated, scoped APIs as any other client, with approvals on actions that change records or money.
Assessment & discovery
Legacy systems carry years of decisions that live only in code and habit. The assessment turns that into evidence you can plan against.
Document the workflows, rules, reports, batch jobs and integrations in use, including the undocumented ones users depend on.
Assess code structure, test coverage, dependencies, security exposure, infrastructure and operational incidents, with severity ranked against business impact.
Use logs and stakeholder input to separate high-value capability from features that can be retired instead of migrated.
Identify regulatory, contractual, data residency and staffing constraints, and who decides each tradeoff.
Migration engineering
Most modernization failures come from cutovers that are too large to test and too entangled to roll back. We design the path so each step is small enough to prove.
Pin current behaviour with tests and recorded outputs, so differences are visible and deliberate rather than discovered by users.
Old and new run side by side behind a routing layer, with traffic moved by capability, user group or percentage.
Where outputs matter, new and legacy paths process the same inputs and results are compared before the new path becomes authoritative.
Each cutover has a tested rollback, a decision owner and criteria agreed in advance.
Data migration & integrity
Data outlives code. Migration is treated as an engineering workstream with its own tests, rehearsals and sign-off.
Field-level mappings, transformation rules and handling for nulls, duplicates and invalid records are documented and versioned.
Full migrations run repeatedly against production-like copies, timed and reconciled, before the real cutover.
Counts, totals, checksums and sampled record comparisons confirm completeness. Discrepancies are explained, not averaged away.
Historic data that does not need to move is archived in an accessible, access-controlled form, aligned with your retention obligations.
Security, reliability & operations
Modernization should close known risks, not just move them. Operations are part of the scope from the first increment.
Supported dependencies, modern authentication, least-privilege access, secret management and logging replace inherited gaps.
Structured logs, metrics, traces and alerts give your team visibility the legacy system often lacked.
Version control, CI pipelines, automated tests and repeatable deployments replace manual release steps.
Retired components are switched off, access removed and infrastructure released, so the old system does not linger as hidden cost and risk.
Technology & frameworks
We work inside your existing environment where it is sound, or select a small, maintainable stack for the build. These are options, not a requirement to use every technology on the list.
Map structure, complexity and risk; AI summaries are verified by engineers.
Pin legacy behaviour and compare old and new paths before cutover.
Chosen for your team's skills and existing estate, not novelty.
Let old and new coexist and move traffic by capability.
Versioned, rehearsed migrations with reconciliation evidence.
Used for comprehension, test drafting, translation and new features, within approved data terms.
Repeatable environments and automated releases.
Visibility and access controls the legacy system often lacked.
Provider selection depends on licensing, approved data processing, availability and operational needs. We prefer fewer moving parts over an architecture your team cannot comfortably run.
Delivery approach
You work with engineers inside the delivery process, supported by the wider team. Reviews, demonstrations and scope decisions stay connected to the operating goal.
Inventory behaviour, technical health, usage and constraints, using AI-assisted analysis verified by engineers.
Output: Assessment report, risk register and retire/keep/change map.
Choose a strategy per capability, define the target architecture and sequence the migration into verifiable increments.
Output: Modernization roadmap with cutover and rollback plan per stage.
Build characterisation tests, observability and the routing layer that lets old and new coexist.
Output: Behaviour test suite, monitoring baseline and coexistence infrastructure.
Move capabilities and data stage by stage, with parallel runs, reconciliation and staged exposure.
Output: Live capabilities on the new system with reconciliation evidence.
Retire legacy components, transfer operating knowledge and confirm the team can run and change the new system.
Output: Decommission record, runbooks, architecture documentation and handover.
A decision-maker, process knowledge, system owners and the credentials or approvals the integrations need. For sensitive data, bring security, privacy and compliance stakeholders into scoping early.
We show working increments, keep constraints visible and document tradeoffs. Acceptance and release decisions have named owners; a deadline does not turn an unresolved risk into an acceptable one.
Handover & ownership
Your code belongs to you from day one. Handover includes the behaviour we recovered from the old system, so knowledge does not disappear again.
All code, migration scripts and infrastructure definitions in your repositories from day one.
The business rules recovered from the legacy system, verified and written down for the first time in many cases.
The characterisation suite and migration reconciliation records that show what changed and what did not.
Operating procedures, alerting, deployment steps and architecture decisions your team can maintain.
Delivery does not include unlimited maintenance or a service-level commitment. We can agree ongoing development and operational support, or hand over fully to your own team.
Related engineering services
Modernization often leads into new applications, AI features or dedicated engineering capacity.
Questions before starting
Usually not. We design for coexistence so the old system keeps running while capabilities move across in stages. Some cutovers need a short, planned window, agreed in advance.
Rarely as a first choice. Full rebuilds carry the most risk because undocumented behaviour gets lost. We recommend a rebuild only when the assessment shows the existing code cannot be safely changed.
To read, explain, map and draft: summaries, dependency maps, characterisation tests and translated code in small units. Engineers review everything and verify it against the running system. Nothing is merged on model output alone.
Only with your approval of the provider, region and retention terms. Where policy does not allow it, we use self-hosted models or work without AI assistance for that material.
It depends on size, data and integrations. The assessment produces a roadmap with staged estimates; each stage delivers working capability, so value does not wait for the end.
That is common. We recover behaviour from code, data, logs and users, then pin it with tests before changing anything.
Yes. A modernized system with clean data, explicit APIs and permissions is a sound base for search, document processing, assistants or governed agents.
No. We set measurable goals, rehearse cutovers and plan rollbacks to reduce risk, but outcomes depend on the system, data and decisions involved.
Start with the system
Tell us what it runs, what is breaking and what must not stop. We will agree a practical first sprint.