01
Recover
We point AI at your existing codebase and extract what it actually does — as user-facing stories, not vague tickets. Real flows, real edge cases, the behavior your users depend on but that was never written down.
AI Engineering
AI that ships, not AI hype.
We are not here to wire up another form-to-email automation. We use AI where it changes the economics of real software — recovering lost business logic from legacy code, accelerating delivery, and shipping production features faster without cutting engineering corners.
Our approach · Code-to-Spec ( C2S )
Your legacy system knows things no one wrote down.
Most legacy systems carry years of business logic that was never documented — and the people who understood it have moved on. Rewrites stall because nobody can say what the old system actually does. C2S is how we solve that: we use AI to read your existing code and reconstruct its real behavior as clear, testable specifications. Then we lock that behavior with tests and migrate to a modern stack — without a risky rewrite from scratch, and without losing the logic your business depends on.
02
Lock
Each recovered behavior becomes a test. We build a test suite around the current logic so it is captured and protected before anything changes. Nothing gets lost in translation.
03
Re-platform
With behavior locked by tests, we rebuild on a modern stack — new language, new framework, new architecture — while the tests prove the old logic still holds. Migration without the guesswork.
The hardest part of modernizing legacy software is not the new code — it is knowing what the old code was supposed to do. C2S turns that unknown into something you can read, test, and trust.
Where C2S fits
When this is the right tool.
- ✓ Legacy mobile and enterprise apps where the original spec is gone
- ✓ Migrations where business logic must survive a platform change (e.g. Xamarin → .NET MAUI, or a full re-platform)
- ✓ Ongoing products where business analysts write stories with no view into how the code actually works — C2S gives them that context, so estimates and stories are right the first time
In practice
AI inside the engineering loop — not bolted on after.
Beyond C2S , AI is part of how our senior engineers work every day. Used with judgment, it compresses the repetitive parts of delivery so the team spends its time on the decisions that actually matter.
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Code understanding & onboarding
AI-assisted analysis of unfamiliar or undocumented codebases, so new engineers reach productive output in days, not months.
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Documentation & specs
Auto-generated technical documentation and specifications kept in sync with the code.
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Test generation
AI-assisted test coverage, including UI automation for flows that are tedious to reach by hand.
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Code review & quality
AI-augmented review that catches issues earlier in the cycle.
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On-device & integrated ML
Bringing models into the product itself where it improves UX, with privacy and cost handled deliberately.
We treat AI as an accelerator, not a replacement for engineering judgment. We do not chase hype automations that look impressive in a demo and break in production.
Selected work
AI features, shipped.
A sample of AI-driven products our engineers have built. Details are anonymized.
Legacy modernization · AI
Legacy logic recovery
Our engineers have used AI to reverse-engineer an undocumented enterprise codebase — reconstructing its real behavior as testable user stories, then locking that logic with an automated test suite ahead of a platform migration. Work no one had specs for became something the team could read, test, and safely re-platform.
Legacy modernizationAI code analysisTDD.NET
Mobile · AI
AI lead qualification assistant
Our engineers have built a mobile lead-processing app that captures business cards via OCR, enriches and scores leads with an LLM, and generates CRM-ready records with follow-up recommendations — turning a manual post-event chore into an automated pipeline.
MobileOCROpenAICRM integrations
Support · RAG
Support knowledge assistant (RAG)
Our engineers have built AI support assistants that answer product questions from internal knowledge bases using retrieval-augmented generation, generate customer-facing responses, and escalate complex cases to humans — scaling support without sacrificing answer quality.
RAGVector searchPostgreSQL + pgvector.NET
Next step
Have a legacy system, a migration, or an AI feature in mind?
We will tell you directly whether AI is the right tool for the job — and where it is not. No hype, no pressure. Just an honest read on the most effective next step.