Structured semantic reasoning for high-stakes domains
AI that reasons over complex information, not just retrieves it.
Kacti AI structures the full picture: facts, relationships, gaps, and contradictions. So you can reason with what's actually there and know what's missing.
Built by a founder who architected AI and enterprise systems at Microsoft, and saw firsthand why current approaches fail on complex reasoning.
The retrieval problem most AI systems ignore
Most RAG systems today work the same way: retrieve fragments, hand them to a language model, hope the answer is complete. The language model has no way to know if the retrieval missed something critical. It reasons over whatever it gets, even if that's only half the picture.
This works for simple questions. It fails on complex analysis: legal cases with hundreds of documents, research synthesis across dozens of papers, enterprise decisions spanning multiple policies. In these scenarios, incomplete retrieval produces incomplete reasoning. And no one tells you what's missing.
Kacti AI closes this gap. Our AI Analyzer doesn't just retrieve and summarize. It structures information semantically, traces relationships across documents, detects gaps in the evidence, and tells the reasoning layer exactly what it found and what it couldn't find.
How the AI Analyzer Works
Ingest and structure complex information
From documents to a connected semantic model
Feed the Analyzer case files, contracts, research papers, or policy documents. It extracts entities, events, claims, and relationships into a structured knowledge graph with typed, directional connections, not just keyword embeddings.
Reason over the full structure, not fragments
Retrieval guided by reasoning, not just similarity
Our retrieval engine navigates the knowledge graph with semantic understanding of the query. It follows multi-hop connections, evaluates relevance at each step, and assesses completeness as it goes, so the generation layer knows what was found and what's missing.
Surface what's there, what's missing, and what conflicts
Every analysis tells you what it couldn't find
The Analyzer outputs structured reasoning artifacts: claim maps, evidence graphs, timeline models, gap analysis. Each comes with confidence scores and an audit trail. You see not just the answer, but the reasoning path and its limitations.
Legal Case Intelligence
Legal is our first proving ground because it's where incomplete analysis has the highest cost. A missed precedent, an undetected contradiction, a gap in the evidence chain. These aren't inconveniences, they're case outcomes. We're building with a litigation team on a real case, structuring facts, mapping claims to legal standards, and stress-testing arguments before opposing counsel does.
Why we're building this now
Language models can reason and knowledge graphs capture structure and complex relationships. What's missing is the reasoning layer between retrieval and generation. Major RAG frameworks (GraphRAG, LightRAG, and their variants) still decouple these phases. We've designed a novel architecture that unifies them, so retrieval itself becomes a reasoning task, not just pattern matching over embeddings.
Early Access Program
We work directly with design partners on active cases. Our first partners include a litigation attorney and a paralegal advisor with experience across multiple law firms and government prosecution offices.