AI Analyzer
The missing reasoning layer between retrieval and generation
The Kacti AI Analyzer structures information semantically and reasons over the full picture before generating any output. It tells you if the retrieval was complete, if critical connections were missed, and if the evidence contradicts itself.
Why "retrieve and generate" isn't enough
In current RAG systems, the retrieval and reasoning phases are decoupled. A retriever finds content using vector similarity or keyword matching. A language model reasons over whatever it receives. Even in systems where it can request more information, those requests still pass through a retrieval layer that relies on vector similarity or keyword matching. The retrieval itself has no understanding of the query, no ability to evaluate what's relevant, and no way to assess whether the results are complete.
This blind spot is manageable for simple factual questions. It's catastrophic for complex analysis: legal case preparation requiring evidence across hundreds of documents, research synthesis spanning dozens of papers, or enterprise decisions that depend on connecting policies, precedents, and data across organizational silos.
Structured analysis, not probabilistic summaries
Builds a semantic knowledge graph from your documents
Extracts entities, events, relationships, and claims into a typed, directional knowledge graph. Every edge carries domain-specific semantics. In legal: cites precedent, overrules, applies statute. In research: supports hypothesis, contradicts finding, extends method.
Navigates the graph with reasoning, not pattern matching
A dedicated reasoning engine traverses the graph based on query intent, not just embedding similarity. It follows multi-hop connections, evaluates relevance at each step, and decides when it has enough, or when critical information is missing. This is retrieval as a reasoning task.
Detects gaps and contradictions
The Analyzer identifies what it could not find, what remains uncertain, and what conflicts exist in the evidence. It communicates retrieval completeness to the generation layer, a capability we haven't found in existing systems.
Produces inspectable, traceable outputs
Every analysis generates structured artifacts: claim maps, evidence graphs, timeline models, gap reports. Each comes with confidence scores and a retrieval audit trail showing which paths were explored, which were relevant, and which came up empty.
Why we built a new architecture
Systems like GraphRAG and LightRAG build useful knowledge graphs, but their retrieval still relies on algorithmic methods: vector similarity, keyword matching, community detection. They retrieve content. They don't reason about it during retrieval.
For complex analysis, existing graph retrieval systems face a core tradeoff: methods that scale well don't understand query semantics, and methods that understand semantics don't scale for iterative exploration. Our architecture resolves that tradeoff.
Legal Case Intelligence
The AI Analyzer applied to litigation. Structure the full case, trace evidence chains, detect gaps, and stress-test arguments before opposing counsel does.
Learn more about Legal Case Intelligence →See the Analyzer in action
We're working with early design partners on active cases. Talk to the founder.