Automotive Software Built on Legacy Systems. Modernized Without Losing Safety-Critical Logic
EltegraAI extracts what your system actually does before generating a single line of new code
Built for Automotive Software Complexity
Automotive software teams are managing safety-critical systems in C and C++ that have accumulated decades of edge cases, calibration logic, and ISO 26262 constraints. Rewriting these systems without fully understanding what they do creates regulatory and safety risk. EltegraAI extracts the embedded logic — every safety function, every exception, every compliance dependency — and locks it before any new code is generated. Modernization that doesn't guess at what the old system did.
Legacy Code Analysis
Automated reverse engineering of undocumented C, C++, and embedded systems. Every safety function mapped before modernization begins.
ISO 26262 Logic Preservation
Safety-critical constraints are extracted and locked into the knowledge graph. Compliance logic is verified, not assumed.
Automotive Knowledge Graph
Business rules, calibration logic, and system dependencies captured in a patent-pending deterministic model. 99.9%+ accuracy.
Modern Code Generation
Production-ready code generated from verified intent. Target language, target architecture — no guesswork.
We read your existing system and map every rule, exception, and edge case — before a single line of new code is written. The system tells us what it does. Not what it was supposed to do.
Business logic is extracted into a patent-pending deterministic knowledge graph — locked and verified before rebuild begins. This is what separates EltegraAI from tools that guess.
Clean, production-ready code generated from verified intent — in your target language and architecture. No hallucinations. No missing edge cases. No 18-month surprises.
See What EltegraAI Extracts From Your Automotive Codebase
Book a 30-minute session. We'll show you exactly what business logic and safety constraints live in your legacy systems — before you commit to a modernization path