Modernize the work, not just the code.
Legacy is not only old code. It is the claims form that gets re-keyed, the approval chain that lives in email, the spreadsheet that runs month-end. We use AI to understand what these systems and processes actually do, then rebuild them step by step without stopping the business.
Transform
What we modernize
Process digitization
Paper, PDF and email-driven processes turned into structured digital workflows with audit trails.
Intelligent document processing
Extraction and validation for invoices, KYC files, claims, contracts and forms, with people reviewing the exceptions.
Legacy code comprehension
AI-assisted mapping of large codebases: dependencies, data flows and the business rules buried in them, written down for people.
Incremental replacement
Strangler-pattern migration: new services take over one capability at a time while the old system keeps running.
Characterization testing
Tests generated from current behaviour, so the new system provably does what the old one did where it should.
Data migration and reconciliation
Data moved out of legacy stores with automated reconciliation, so nothing is lost or counted twice.
How it runs
From first week to handover.
Durations are typical. We agree the actual plan with you during scoping.
Assess
2 to 3 weeksWe map the process and the systems behind it with AI-assisted code and document analysis. You get a modernization map ranked by value and risk.
Prove
4 to 6 weeksOne slice modernized end to end, running alongside the old process so results can be compared.
Scale
3 to 9 monthsCapability-by-capability migration with characterization tests and reconciliation at every step.
Retire
As each slice landsOld components are switched off once the new ones have matched them in production.
AI in the loop
Where AI changes the work
Reading what nobody documented
Models summarize modules, trace data through old code and draft the business rules for your experts to confirm.
Turning documents into data
Extraction models handle varied layouts and handwriting, with confidence scores that decide what a person checks.
Tests from today's behaviour
AI generates characterization tests from production samples and logs, which makes refactoring safe.
Faster, safer rewrites
Agents translate and refactor code under engineer review, while the test suite holds behaviour fixed.
What you own at the end
- A documented map of your current process and systems
- Business rules written down and confirmed by your experts
- Modernized workflows running in production
- Characterization and regression test suites
- A retirement plan for the remaining legacy components
Ways to engage
Readiness sprint
A defined question answered: an assessment, an architecture review or a scored use-case portfolio.
Pilot to production
One use case built on your data and released to real users, against success measures agreed up front.
Forward-deployed pod
A small senior team embedded in your business that owns an outcome end to end.
FAQ
Questions we hear.
Do we have to stop using the old system?
No. We replace it one capability at a time, and old and new run side by side until the new one has proven itself.
Our system is COBOL, VB6 or an old Java stack. Is that a problem?
No. AI-assisted comprehension works across older languages, and our engineers verify what it finds. The target stack is chosen with you.
Can you work with scanned and handwritten documents?
Yes. Extraction quality varies by document type, so we measure it on a sample of your real documents during the assessment before committing to a target.
How do you handle regulated data?
Processing runs in your environment or approved regions, with access controls, audit logs and data handling agreed up front. We sign data processing agreements and work within GDPR and India's DPDP Act.
Related
Often paired with.
Build
Platform & Data Engineering
Cloud-native platforms, event-driven systems, payments and data infrastructure that stay reliable at scale and are ready for AI workloads.
Transform
AI Strategy & Deployed Consulting
Consultants who work inside your organization to decide where AI pays off, put the governance in place to use it safely, and stay through delivery.
What are we building?
Tell us about the problem. An engineer, not a sales team, will reply.