Why the Knowledge Graph Outperforms Generic AI — At Any Scale
Eltegra builds a deterministic, dependency-linked knowledge graph — not probabilistic retrieval — enabling near-complete reconstruction of system intent at more than 99.9% accuracy, even for multi-million-line codebases. Here's why that matters.
No Semantic Search Gaps
Our patent-pending approach processes repositories of any size with complete, accurate logic capture. No reliance on semantic search — ensuring no gaps in business logic, no matter how large the codebase.
99%+ accuracy vs. 60–75% for LLM-based tools on large codebases.
01
Multiple Passes. Verified Completeness
Most AI tools do a single-pass analysis. EltegraAI runs multiple extraction and validation passes to achieve complete capture of business logic, dependencies, and rules
02
Code Is Only Part of the Story
EltegraAI fuses source code, documentation, Jira tickets, meeting transcripts, operational artifacts, and human expertise via automated interviews — creating a far richer representation of business intent than code-only approaches.
03
Frozen Intent: Validated Before Code Is Generated
EltegraAI constructs a comprehensive model of your system's functional and business behavior before generating any output. Every transformation flows from this validated intent — ensuring full consistency and traceability throughout.
Same frozen intent that drives the C# code drives the React UI — same business rules, same data, guaranteed.
04