AI-Powered Modernization Framework
Know your legacy.
Before you touch it.
Many modernization projects get stuck already in tech stack discovery. Our framework analyzes your entire codebase, producing a complete migration blueprint: architecture findings, per-component specs, a wave-based execution plan, and behavioral equivalence checks. All traceable to the source.
EXECUTIVE CHALLENGES
The real reasons modernization projects stall - or get stuck entirely
If any of these sound familiar, you're not alone. These patterns repeat across industries, team sizes, and tech stacks.
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Discovery consumes months before anything changes
Architecture interviews, manual code walkthroughs, and workshops just to understand what you own - before a single ticket is written.
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Nobody has a complete map of the system
Documentation drifted from reality years ago. Business rules hide in untested paths. Surprises surface mid-migration and rewrite timelines.
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Risk is impossible to quantify before commitment
Without evidence-grounded analysis, risk registers are guesswork. Boards need confidence - and gut-feel architectures don't pass scrutiny.
Estimates blow up once real work begins
The first sprint reveals dependencies no one knew existed. Scope expands. Velocity drops. The original roadmap becomes a liability.
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Migration knowledge lives only in people's heads
Senior engineers carry institutional knowledge that isn't documented. When they leave or get reassigned, the program loses its compass.
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No way to prove the new system behaves correctly
Without systematic equivalence validation, teams either over-test manually or trust that "it looks the same." Neither gives boards confidence to decommission.
The AI-Powered Modernization Framework was built to solve exactly these problems.
REAL-WORLD CASE STUDY
Proven on a FinTech Platform
We applied the framework to a large banking platform - a Java monolith with thousands of files, multi-tenant architecture, 20+ locales, and mandatory audit columns - targeting a full microservices rewrite on cloud infrastructure.
~2 Hours
Analysis completed
7,000+
Files
analyzed
194
Document pages assessed
33
Assessment sections generated
Curious what this looks like for your stack?
We'll walk you through a scoped analysis on your own codebase.
HOW IT WORKS
Four phases. One coherent system.
Not a collection of scripts - a structured framework from raw codebase to deployed microservices, with evidence and human review gates at every step.
PHASE 1
Your codebase becomes searchable knowledge
Connect repos in one click. Every file ingested into a vector store - queryable by AI, never browsed from disk.
PHASE 2
A complete, evidence-grounded picture of your system
14 quality lenses. 7-dimension readiness snapshot. Every finding traced to a file, line, and commit SHA.
PHASE 3
Parallel AI agents execute the migration, wave by wave
Specs feed BA, architect, developer, and tester agents simultaneously - with human review gates before each wave.
PHASE 4
Drift detected automatically as your codebase evolves
Every re-run is versioned and diffable. A drift register flags where code and documentation diverge.
Want to see the full framework in action on a real codebase?
WHY AI CHANGES MODERNIZATION
This isn't AI-assisted. It's AI-native.
Traditional firms apply human consultants to problems that scale poorly with humans. Our framework inverts this - AI handles the scale, humans set the direction and approve every gate.
Reads the entire codebase simultaneously
Human architects sample. The framework reads everything - no blind spots, no missed dependencies, no business rules hiding in untested paths.
Every finding traces to a source line
Each finding links to a file, line, and commit SHA. No assumptions. No hand-wavy diagrams that don't survive contact with reality.
From zero to full migration plan in a fraction of the time
Architecture analysis, gap assessment, roadmap, per-component specs - produced in a single automated run on a production-scale codebase.
Risk surfaced before commitment, not during
14 lenses across security, resilience, observability, compliance and more. Stakeholders see the real risk profile before any budget is approved.
WHY CREATEQ
We built the tool we wished existed.
CREATEQ built this framework after watching the same failure modes repeat — slow discovery, incomplete analysis, migration plans that don't survive first contact with the real codebase.
Not a black box
Every parameter tunable. Quality-lens handlers configurable. Document templates versioned. You see exactly how analysis was produced - and can reproduce it.
RAG-only - no filesystem access required
Reads your codebase through its own knowledge base. Consistent, reproducible, and doesn't expose your systems to external reads.
Works inside your existing AI tooling
Supports the Model Context Protocol - connects to Claude, GPT, or any MCP-compatible AI. Your teams stay in their preferred environment.
Analysis flows directly into working code
Migration specs feed a wave-based implementation framework with parallel AI agents - continuous from analysis to deployed microservices.
Equivalence validation built in, not bolted on
Behavioral parity validated via API comparison. Know the refactored code does exactly what the original did before decommissioning anything.
What you receive
A structured set of outputs covering every stage of your modernization workflow.
Analysis PDF
33-section navigable report — findings, architecture, migration strategy, execution plan
Refactored code
Generated from specs, ready for review and test
Equivalence checks
API-level validation ensuring behavioral parity
Works with any AI via MCP
Connects to Claude, GPT, or any MCP-compatible AI. Your team stays in their preferred environment.

Ready to see what's actually in your legacy system?
Choose the conversation that fits where you are - we'll take it from there.