Lyriks.io

By 2030, AI tokens will represent up to 50% of software project costs. Yet most teams keep losing the value of that spend: specs drift, AI agents hallucinate business rules, and what ships rarely matches what was promised. The bottleneck isn't the model. It's the absence of a shared, executable source of truth across humans and AI.

Today's tools work around this. Linear, Notion, and .cursor/rules files version conventions and tickets, but business logic lives in heads, Slack threads, and prose documents that LLMs reinterpret on every prompt. The faster AI ships code, the wider the gap grows.

Lyriks introduces a coherence layer. Product managers, developers, and AI agents (Claude Code, Cursor, and any MCP-compatible tool) work on the same executable specification : a semantic graph that holds business rules, invariants, and dependencies. Contradictions are detected before code is written. The same spec can be reverse-engineered from existing codebases, giving teams a way to understand and safely modify legacy or AI-generated systems.

The result: fewer token round-trips with the LLM, releases that match commitments, and a measurable coherence metric next to DORA, the industry standard for engineering performance.

Lyriks is built on ten years of CNRS research in categorical graph rewriting, protected by patent. It's not another LLM wrapper. It's the structural layer that AI-native software was missing.