“The future of AI isn’t about searching siloed information faster through a natural‑language interface - it’s about continuously and semantically unifying tacit human knowledge and siloed data into a ground truth from which AI Agents can expertly reason, create new knowledge, and build on accumulated knowledge to produce insight that didn’t exist in the training text. It’s a revolution in human reasoning and decision automation and it all starts with the Knowledge Engine and its ability to capture and scale tacit human knowledge.” -Prashant Bhuyan, Founder, CEO and Chairman of Accrete, Inc.
Large Language Models (LLMs) are astonishing word artists. They predict, paraphrase, and draft at blistering speed - yet when the stakes rise, pure pattern‑matching fractures. Domain accuracy slips, explainability fades, and decisions stall. Accrete’s Knowledge Engines go further: they codify tacit human expertise, fuse siloed enterprise data into a dynamic knowledge graph, and ground every AI decision in verifiable truth.
From Siloed Data to Unified Ground Truth
Enterprises don’t just need faster search; they need certainty. Knowledge Engines ingest heterogeneous sources - databases, documents, messages, sensor feeds, expert annotations - and continuously reconcile them into a living, semantic representation of "what the organization knows right now." This ground truth lets Expert AI Agents:
- Reason across implicit relationships that RAG pipelines miss.
- Create new knowledge by validating or refuting LLM‑generated hypotheses against the graph.
- Build on prior insight as the graph evolves, compounding institutional intelligence over time.
LLMs Are the Interface - The Knowledge Engine Is the Brain
“LLMs aren’t enough to transform enterprises. Rather, they are merely a useful interface for AI Agents. To scale human expertise orders of magnitude, LLMs need digital brains with persistent memory called Knowledge Engines.” -Prashant Bhuyan
LLMs excel at natural‑language interaction, but they lack persistent memory and structured reasoning. A Knowledge Engine provides both by:
- Structuring Facts & Context - via an autonomous knowledge graph that captures entities (Supplier, Facility, Person) and relationships (ShipsTo, MentionedIn, SanctionedBy).
- Persisting Expert Judgement - tacit rules, heuristics, and labels applied by analysts become machine‑readable knowledge functions that propagate across the graph.
- Orchestrating Expert AI Agents - domain‑specific agents (e.g., supply‑chain risk, narrative detection) query the graph, enrich it with new findings, and return audit‑ready answers.
Architecture Built for Complexity
- Knowledge Graph - Semantic backbone capturing explicit & inferred relationships; continuously updated.
- Expert AI Agents - Task‑specific reasoning engines that leverage graph context to solve nuanced problems LLMs alone cannot.
- LLMs - Natural‑language interface and hypothesis generator, grounded by the graph for accuracy & traceability.
The Tangible Edge: Quantified Impact
- 54 % vs. 16 % accuracy - In an internal benchmark, LLMs grounded in a knowledge graph answered enterprise SQL questions with 54.2 % accuracy, compared with 16.7 % for the same LLM without graph grounding.
- Traceable reasoning - Every output is auditable back to nodes and relationships in the graph.
- Zero retraining lag - Modify a fact in the graph and downstream AI behavior updates instantly. No model fine‑tuning cycles.
- Domain‑level guardrails - Graph semantics act as policy controls, reducing hallucinations and ensuring security and compliance boundaries.
Case Study: U.S. Department of Defense
On a multi‑year contract with the DoD, Accrete’s Expert AI Agent Argus assesses foreign social‑media influence and supply‑chain risk from open source intelligence (OSINT). Traditional RAG would require thousands of look‑ups per query. Argus instead extracts and normalizes relationships into a graph, enabling rapid, contextualized threat assessments. Analysts label entities (e.g., “under foreign influence”) once, and Argus propagates that judgment across the graph - scaling human expertise via knowledge functions.
The Coming Revolution in Decision Automation
Human decision‑making is constrained by siloed data and finite cognition. Knowledge Engines break both limits by encoding tacit knowledge into a machine‑readable substrate. Enterprises that deploy them don’t just answer questions faster; they automate reasoning itself, achieving superhuman scale, speed, and consistency.
Ready to see how a Knowledge Engine becomes your organization’s digital brain? Reserve your spot in our limited pilot program today.