Knowledge Engine
OS
KEOS is a research framework for self-maintaining knowledge bases. It captures what your agents and analysts discover, keeps it current, and lets humans verify how every insight was derived.
The Triple-Layer Architecture
Deterministic retrieval through structural rigor.
Resource Layer
Multi-modal ingestion of unstructured docs, logs, and databases into a bitemporal fact store.
Semantic Layer
Multi-layered extraction identifying entities, relations, and semantic clusters across high-dimensional space.
Assertion Layer
Logical verification and conflict resolution using Quantitative Bipolar Argumentation for deterministic output.
Hybrid RRF Retrieval
Combines vector search (pgvector), graph traversal (FalkorDB), and SQL filters using Reciprocal Rank Fusion.
Tiered Extraction
Routes extraction through a hierarchy of optimized local and frontier models to minimize indexing overhead.
MCP-Native
Integrates directly with the Model Context Protocol for tool-calling and data interoperability.
Data Sovereignty
Model-agnostic architecture designed for on-premise VPCs or secure hybrid-cloud environments.
Reciprocal Rank Fusion (RRF) Orchestration
KEOS doesn't rely on a single retrieval method. It orchestrates results from vector similarity, graph traversal, and SQL-based metadata filters, merging them into a single, high-confidence context window.
Eliminates flat vector semantic drift
Supports multi-hop reasoning over knowledge graphs
Metadata-aware filtering for strict compliance
Bitemporal awareness for time-sensitive facts
Infrastructure Accelerator vs. SaaS Wrappers
Why engineering the foundation matters for enterprise reliability.
Ready to compound your
organizational intelligence?
Explore how the KEOS research framework could work in your secure environment and what a self-maintaining knowledge base would look like for your data.