Turn the system you can't replace into the system AI can run on.
Assess, refactor, embed AI, migrate. A structured programme that modernizes legacy software in stages, with the business running throughout.
What is legacy-to-AI modernization?
Legacy application modernization updates an ageing, business-critical system without a risky rewrite. Eazyware's ReCore programme assesses the code and data, refactors in slices using the strangler pattern and API façades, embeds AI capabilities such as copilots and document extraction on the stabilised core, and migrates infrastructure with rollback at each step.
| Service line | Enterprise Software & Modernization |
|---|---|
| Engagement | Fixed-price program (ReCore) |
| Duration | 8–16 weeks |
| Starting price | $31,500 |
| Typical range | $31,500 – $105,000+ |
| Deliverables | 6 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
The core system is eight years old, undocumented, and everything depends on it. A rewrite is a two-year bet nobody will approve. Doing nothing means no AI, rising cost and a shrinking pool of people who understand it.
How do we approach it?
ReCore starts with a two-week assessment that maps what is actually there: modules, dependencies, data, integrations, the cron jobs nobody remembers, and a risk-and-effort rating for each part. Then characterisation tests are written against the live system's real behaviour, so every change can be checked against what the system does rather than what people think it does. An API façade wraps the legacy application, turning replacement into a sequence of independent choices. Modules are migrated or replaced one at a time behind the façade, scheduled around your business calendar, each with a rehearsed rollback. AI capabilities, extraction, search, copilots, are added on the stabilised core, and documentation is regenerated with AI assistance and reviewed by your team.
What do clients use it for?
- Old PHP, .NET or Java systems that everything depends on
- Monoliths that need APIs for AI and partners
- Database and framework upgrades without downtime
- Adding copilots and extraction to legacy workflows
Is it the right fit?
Good fit when
- Enterprises with business-critical legacy software
- Teams that cannot afford a rewrite
- Companies blocked from AI by their current stack
Probably not when
- Systems scheduled for decommissioning
- Greenfield products
What do we build?
- Assess: code, data, integrations, risks, cost of change, modernization map
- Refactor: strangler pattern, API façade over legacy, module extraction, test coverage
- Embed AI: copilots, extraction, search and agents on top of the stabilised core
- Migrate: cloud, database and framework upgrades in slices with rollback
- Data cleanup and migration
- AI-assisted documentation regeneration for the parts nobody documented
What you get
- Assessment report
- Modernization roadmap
- Refactored modules
- AI capabilities live
- Migrated infrastructure
- Documentation
How does the engagement work?
- 01
Sprint Zero assessment
- 02
Roadmap and fixed-price phases
- 03
Phase delivery with sign-off per slice
- 04
Handover
What does good look like?
A system on a supported stack, with tests where there were none and documentation where there was folklore, that never stopped serving the business while it changed. AI features live on data that used to be locked in a monolith. Infrastructure your team can rebuild from code. And a façade that makes the next modernisation decision yours to take, with or without us.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | 8–16 weeks | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Modernisation fails when it becomes a rewrite by stealth, when changes are made without a test safety net, when cutovers happen during business-critical periods, and when the new system has no owner. We refuse the rewrite, test first, schedule around the calendar and pair with your team throughout.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Modules migrated per phase
- Business-hours downtime (target: zero)
- Infra and licence cost change
- AI capabilities added
Which technologies do we use?
- Stack-agnostic assessment
- Target: Node.js / React / Postgres or your standard
- AI layer from Agents and Intelligence lines
Who does the work?
An architect who leads the assessment, two engineers who can read the legacy stack as well as build the new one, an AI engineer for the capabilities added, and a delivery lead who manages the calendar with your business owners.
What do you need to bring?
Access to the codebase, database and infrastructure, someone who knows the system's history, the business calendar with the periods when nothing may change, and an IT lead who will own the system afterwards and pair with us during the work.
Frequently asked questions
Do we have to rewrite?
Almost never. The strangler pattern and façades let you modernize in slices.
Downtime?
Phased cut-overs with rollback. Typically zero business-hours downtime.
Old PHP, .NET or Java?
Yes. Assessment is stack-agnostic.
Where does this fit?
Legacy-to-AI Modernization Program is part of our Enterprise Software & Modernization line. See all pricing or talk to an engineer.