Platform
Modernize with confidence, not guesswork
Our AI-powered platform combines deep codebase analysis, automated feature discovery, and BDD-driven implementation to transform your legacy systems.
The Repave Approach
Modernize with confidence, not guesswork
Repave.ai combines AI analysis, deep codebase understanding, and automated BDD implementation to transform your legacy system into modern software — with every business rule preserved and tested.
AI-Powered Deep Analysis
Our AI agents analyze your entire legacy codebase — every file, function, database table, and business rule — capturing institutional knowledge before it's lost.
- Automatic architecture extraction (C1-C4 diagrams)
- Database schema and ER diagram generation
- Business feature discovery and documentation
- Complete dependency and call-chain mapping
Legacy Codebase
1,200 files analyzed
AI Analysis Engine
Architecture, DB, Features
Complete Documentation
C4 diagrams, ER, Features
Complete Legacy Understanding
We map every actor, action, interface, function, and database table in your system — revealing how your codebase truly works so nothing gets lost in translation.
- Full traceability from users to database tables
- Business rule extraction and documentation
- Multi-language support (Java, COBOL, PHP, .NET, Python)
- Visual exploration of system relationships
Full System Traceability
Every path from user to data — fully mapped
Automated BDD Implementation
AI agents implement each discovered feature using Behavior-Driven Development with Gherkin scenarios. Every feature is built with comprehensive test coverage from day one.
- Auto-generated Gherkin scenarios from legacy code
- AI-driven implementation with 90%+ test coverage
- Isolated development per feature with regression testing
- Modern stack output (React, Next.js, APIs)
Feature: Order Management
As a sales representative
Scenario: Create new order
Given I am on the orders page
When I fill in order details
Then the order should be saved
The Repave 5D Framework
Define. Discover. Design. Develop. Decision.
You set the rules and make the final call — Repave handles the discovery, design, and development in between. Every stage is reviewable before the next begins.
Define
Before any code changes, you set the guardrails the whole modernization obeys — the validation rules every AI agent must follow, and your database policy for which schemas and stored procedures to retain versus modernize. Decided once, enforced automatically downstream.
In the app: Validation Rules · DB Policy
Output: Governance and data policy the AI is bound by
Explore DefineDiscover
AI reads the entire legacy codebase and builds a complete, reviewable inventory of what it actually does — requirements composed into a BRD, features written as legacy-grounded BDD scenarios traced to source lines, plus every screen, API, batch job, data store, and the as-is architecture. Nothing is invented; everything cites source evidence.
In the app: Requirements · BRD · Discovered Features · Views · APIs · Data Stores · Batch Jobs · As-Is Architecture · User Journeys
Output: A source-traced map of the legacy system
Explore DiscoverDesign
With the legacy system understood, Repave designs the modern target — the to-be architecture and tech stack, a design system, and a reusable application shell with interactive Figma prototypes — plus to-be catalogs for every API, view, batch job, and table. Stakeholders sign off on architecture and UX before implementation begins.
In the app: To-Be Architecture · Design System · Application Shell · UI Prototypes · To-Be Catalogs · Impact Analysis
Output: The agreed modern blueprint, visualized in Figma
Explore DesignDevelop
Autonomous agents implement the approved scenarios feature-by-feature on the modern stack, each in an isolated worktree with its own tests and regression checks — bound by the rules from Define and the target from Design. The generated code is browsable in an embedded VS Code.
In the app: Develop Features · Repave IDE
Output: Modern, tested code, feature by feature
Explore DevelopDecision
AI does the work, but a person makes the call. Review each completed feature by replay, run UAT, file and triage bugs against fix evidence, then accept the work or send it back with feedback — the accountability gate regulated teams require.
In the app: Completed Features · UAT Tools · Bug Reports
Output: Accepted, sign-off-ready modernized features
Explore Decision