• Galaxia delivers the deterministic semantic memory layer enterprises need for trustworthy agentic AI: persistent, structured knowledge that scales reliably, explains every decision, and meets regulatory demands like the EU AI Act - powering agents that don't just act, but reason with full provenance.
• As Gartner predicts, 40% of enterprise apps will integrate task-specific AI agents by end-2026 (up from <5% today), evolving to multi-agent ecosystems by 2029. IDC forecasts 80% of agentic use cases requiring real-time contextual access by 2027. Yet over 40% of these projects will fail due to poor risk controls, unclear value, and unreliable memory - exactly where probabilistic RAG collapses.

• 2026 marks the shift from chatbots to autonomous agents that plan, execute, and collaborate across workflows. Yet many early projects are expected to stall due to unreliable context, poor governance, and retrieval degradation as corpora scale.
• Traditional stacks (LLMs + RAG + vector DBs) hit limits:

• Embeddings dilute relevance signals as data grows, missing cross-document dependencies and increasing noise
• Short-term context windows forget critical details across long tasks
• Post-hoc explainability struggles to meet EU AI Act and FDA requirements

• Agentic AI demands verifiable, compounding memory - knowledge that persists, structures relationships explicitly, and traces every inference.

• Galaxia transforms unstructured data (documents, tables, reports) into semantic hypergraphs - a persistent cognitive layer optimized for agentic reasoning:

• 1. Persistent, Domain-Wide Memory

• Ingests 400M+ characters in a single CPU/RAM pass (no chunking, no GPUs).
• Maintains full context across thousands of pages - 1000x mainstream LLM capacities - enabling agents to reason over entire enterprise corpora.

• 2. Explicit Structure & Compositionality

• Nodes: Concepts/entities as first-class objects.
• Hyperedges: N-ary/recursive relations (e.g., "drug interacts with biomarker under condition X in trial Y").

• 3. Deterministic Reasoning with Provenance

• Symbolic-genetic-neural hybrid: Graph traversal + optimization yields auditable paths.
• Deterministic inference over explicit semantics - no model-fabricated facts, only traceable graph facts and rules.
• 95%+ retrieval accuracy (PubMedQA vs. 75-80% SOTA)

• 4. Federated Scalability (Graph Swarm)

• Shards as autonomous hypergraphs (e.g., per department/organization).
• Orchestrator merges reasoning transparently - ideal for multi-agent sovereignty and compliance (data residency, EU AI Act).

• 5. How It Powers Agentic Workflows

• Life Sciences: Agents trace clinical trials → drug interactions + biomarkers with FDA-ready provenance.
• Finance: KYC/AML agents map policies → risks across federated shards.
• Enterprise: Copilots fuse engineering docs + tables for multi-hop reasoning.

• Enterprise pilots validate: production systems in hours / days (vs. 6+ months failed RAG), 1000x deployment speed, 95%+ engineering savings.

• Gartner's Top Trends highlight multi-agent systems and AI security platforms - but without trustworthy memory, 40%+ fail.
• Galaxia closes this gap: unified ingestion → hypergraph construction → auditable inference, all in-memory on CPUs. No more redundant calls. No more lost context. Just structured truth.

• Galaxia is the post-RAG foundation for agentic intelligence that enterprises can trust.

• Request Early Access → Demo on your data:  https://www.smabbler.com/beta
• Read technical overview → Demo on your data:   https://www.smabbler.com/technology-1
• #TrustworthyAI #AgenticAI #SemanticHypergraphs #KnowledgeGraphs #PostRAG #EUAIAct #ExplainableIntelligence