SenseMesh — Evidence-first RAG with wave semantic memory and knowledge graphs
SenseMesh combines wave semantic memory, knowledge graph retrieval and evidence assembly into an evidence-first RAG layer for LLM agents and enterprise AI in high-error-cost domains.
Wave semantic memory
Knowledge graph retrieval
Evidence-first RAG
Grounded LLM agents
SenseMesh
Evidence-first RAG for complex reasoning systems.
SenseMesh is a hybrid retrieval and reasoning layer for domains where semantic precision, provenance and context integrity matter. It combines wave-based semantic memory, knowledge graph structure and evidence assembly to turn fragmented enterprise knowledge into grounded context for AI agents.
Most RAG systems retrieve similar text. SenseMesh retrieves evidence, connects it through graph structure and assembles context that reasoning systems can use with confidence.
What SenseMesh does
Retrieves evidence, not just similar text
SenseMesh retrieves evidence for reasoning, analysis and decision workflows. It preserves meaning-critical distinctions such as assertion vs negation, uncertainty vs evidence, causality vs association, and direct support vs peripheral context.
This matters in domains where a superficially similar passage can still point the system in the wrong direction.
Connects wave semantic memory with knowledge graphs
SenseMesh combines wave-based semantic retrieval with knowledge graph navigation. Wave semantic memory captures nuanced meaning; the knowledge graph keeps evidence connected to entities, relationships, timelines, claims and source context.
The result is semantic search that moves beyond isolated fragments toward structured, traceable evidence paths.
Builds grounded context for LLM agents
SenseMesh assembles compact evidence packages for downstream LLMs, AI agents and enterprise reasoning systems. It grounds responses in selected sources instead of raw retrieval output, reducing the risk of unsupported answers and context drift.
This gives agentic RAG systems a stronger foundation for explainable, reviewable and source-connected reasoning.
Built for high-error-cost domains
Medical research and healthcare
SenseMesh retrieves biomedical evidence while preserving clinical context, uncertainty, mechanism, indication and adverse-effect direction. Wave semantic memory separates meaning-critical signals; graph navigation keeps findings connected to entities, studies and source records.
Genetics
SenseMesh supports evidence retrieval where pathogenicity, uncertain interpretation, evidence maturity and variant context must remain distinct. It connects variant-related evidence across records while preserving the semantic difference between strong evidence and uncertain interpretation.
Pharma
SenseMesh distinguishes treatment signals, safety signals, interactions, mechanisms and clinical actions across complex biomedical corpora. It combines semantic retrieval with graph-linked context to keep drug, condition, adverse event and evidence relationships aligned.
Compliance and KYC
SenseMesh preserves entity identity, risk context, relationships, timelines, evidence strength and provenance across fragmented records. It supports retrieval workflows where traceability, explainability and context continuity are required.
Legal and enterprise knowledge
SenseMesh works with complex documents where claims, obligations, exceptions, timelines and supporting sources must remain connected. It turns enterprise AI retrieval from document search into evidence-centered reasoning context.
Any domain where precision defines the outcome
SenseMesh applies wherever retrieval errors compound into reasoning failures. If your knowledge carries distinctions that must not collapse — between assertion and negation, evidence and hypothesis, risk and context — SenseMesh keeps those boundaries intact across the entire retrieval-to-reasoning chain.
How SenseMesh works
SenseMesh sits between knowledge sources and reasoning agents. It orchestrates semantic search, wave-based retrieval, knowledge graph navigation, ranking and evidence assembly into a unified RAG pipeline.
A query can move through semantic recall, graph expansion and evidence consolidation before reaching an LLM or downstream agent. This supports multi-hop reasoning, grounded answer generation and traceable context construction without forcing a specific model, vector database or agent framework.
SenseMesh can operate as the retrieval intelligence layer inside enterprise AI systems, research tools, agentic RAG workflows and domain-specific knowledge platforms.
Why SenseMesh
Generic RAG retrieves what looks similar. Graph-only systems connect what was extracted. SenseMesh combines wave semantic memory, knowledge graph structure and evidence assembly in one retrieval layer.
That combination is critical when the system must preserve not only relevance, but also meaning, relationships, provenance and evidence quality.
SenseMesh is built for teams that need AI systems to reason over complex knowledge without losing the source, the context or the distinction that makes the answer correct.