Legacy System
Modernization
Your legacy systems are not the problem — the inability to change them is. We modernise mission-critical applications incrementally, with zero downtime and zero disruption to your operations.
Request Free Assessment arrow_forwardOur Modernization Framework — Zero Downtime
We use the Strangler Fig Pattern — incrementally replacing legacy functionality with modern components while the old system continues running. No big-bang rewrites. No risk.
Assess — Map the legacy landscape
Code archaeology, dependency mapping, and data lineage analysis. We identify every integration point, downstream consumer, and hidden business rule before writing a single line of new code.
Wrap — Add an API gateway facade
A thin REST/GraphQL API layer sits in front of the legacy system. New consumers call the modern API; it proxies to the legacy core. The strangler fig takes root.
Migrate — Incrementally re-implement
Business capability by capability, we rewrite functionality in the modern stack — with feature flags allowing instant rollback. The legacy system shrinks; the new system grows.
Validate — Dual-run and shadow testing
Both systems run in parallel. Traffic is compared request-by-request (shadow mode) to catch discrepancies before cutover. Zero surprises.
Cutover — Retire the legacy system
When the new system handles 100% of traffic successfully for a defined period, the legacy system is decommissioned. Teams feel no disruption — the cut happened weeks earlier in the code.
The Strangler Fig in Practice
Named after the Strangler Fig tree that grows around a host, using it for support while gradually replacing it — our modernisation approach keeps your legacy system alive and serving users until the new system is ready to take over completely.
Feature Flag Deployment
Every migrated component is behind a feature flag. Instant rollback in under 60 seconds if any issue is detected. We de-risk every release.
Typical Engagement
First working API wrapper: 2 weeks. First migrated business capability: 6–8 weeks. Full legacy retirement: 6–18 months depending on system complexity.
Detailed Technical Approaches
Every legacy system is different. We adapt our approach to your specific stack, constraints, and risk tolerance.
API Wrapping — SOAP to REST / GraphQL Migration
Legacy SOAP services are wrapped behind a modern API gateway (Kong, AWS API Gateway, or Traefik). We auto-generate OpenAPI 3.0 specs from WSDL files, then build adapter layers that translate between JSON/REST and the legacy XML/SOAP protocol.
- arrow_rightZero changes to the legacy SOAP service
- arrow_rightNew consumers use modern REST or GraphQL immediately
- arrow_rightAPI versioning and deprecation management included
- arrow_rightAuthentication upgraded to OAuth 2.0 / JWT at the gateway
Database Migration — Oracle / DB2 to PostgreSQL
We migrate enterprise databases using AWS SCT/DMS, Ora2Pg, or custom migration scripts for complex stored procedures and triggers. Every migration runs with dual-write validation: data is written to both databases simultaneously, and we compare checksums before cutover.
- arrow_rightZero data loss — validated with row-level checksums
- arrow_rightOracle PL/SQL → PostgreSQL PL/pgSQL conversion
- arrow_rightPerformance benchmarking before and after migration
- arrow_rightLicensing cost savings: typically ₹30–90L/yr for mid-size Oracle deployments
UI Modernization — Legacy Frontend to React / Vue
We wrap legacy UIs (JSP, ASP, WinForms, Delphi) inside modern micro-frontend shells without rewriting business logic. Users get a modern interface; the backend continues to function. Then we incrementally replace screens one by one.
- arrow_rightMicro-frontend architecture (Module Federation)
- arrow_rightLegacy screen embedded as iframe in modern shell initially
- arrow_rightComponent-by-component replacement with user testing
- arrow_rightWCAG 2.1 AA accessibility compliance on all new screens
AI Augmentation — Add Intelligence Without Full Rewrite
Not everything needs to be rewritten. In many cases, legacy systems can be augmented with an AI layer — an intelligent API middleware that adds NLP search, document understanding, recommendation engines, or anomaly detection on top of the existing system, with no changes to the core.
- arrow_rightGPT-4 / Claude API integration for natural language interfaces
- arrow_rightDocument intelligence layer (invoices, contracts, reports)
- arrow_rightSemantic search over legacy data without re-indexing
- arrow_rightTypically deployed in 4–6 weeks on top of any legacy system
Technology Stack
Why Modernize Now?
- trending_upScalability — Legacy monoliths cannot scale horizontally. Modern microservices scale individual components independently, handling 10× traffic spikes without full system scaling.
- trending_upSecurity — Legacy systems accumulate CVEs. Modern stacks with automated dependency scanning and secret management dramatically reduce your attack surface.
- trending_upTalent Acquisition — COBOL, Delphi, and legacy Java developers are retiring. Modern stacks (React, Python, Go) attract a 4× larger talent pool.
- trending_upCloud Economics — Legacy apps running on on-premise hardware cost 2–5× more than equivalent cloud-native workloads when total cost of ownership is calculated.
- trending_upAI Readiness — AI integration requires modern APIs and data pipelines. Legacy systems are the #1 blocker for enterprise AI adoption.
"Organisations that completed application modernisation programmes achieved an average 3-year ROI of 250%, with the largest gains coming from reduced infrastructure costs, faster feature delivery, and improved developer productivity."
What We Modernise
- check_circleAPI gateway wrapper around legacy SOAP systems
- check_circleStrangler fig pattern migration
- check_circleAI augmentation layer (no full rewrite)
- check_circleDatabase migration (Oracle → PostgreSQL)
- check_circleUI modernisation (legacy → React / Vue)
- check_circleFeature flag-based production deployment