Knowledge Engines transform organizations by connecting data, capturing knowledge, and allowing Expert Agents to act on all of it.
Capture and amplify organizational knowledge from your best people by allowing Expert AI Agents to learn, reason, and deliver trusted decisions.
It all begins with the knowledge graph
It semantically unifies internal data siloes and contextualizes all of the information, creating a ground truth of what matters, how things connect, and which actions drive outcomes.
Agents that reason like subject matter experts
Unlike LLMs, the knowledge graph provides a ground truth that enables AI agents to reason effectively – aligning organizations around internal goals and defending against external misinformation.
An army of agents that scale human expertise
Agents continuously update the graph, retaining every insight and interaction. With just a few prompts, prebuilt or custom Expert AI Agents can be deployed across use cases – amplifying human intelligence, decision-making, and business impact. This process transforms the knowledge graph to a Knowledge Engine.
Accrete’s Knowledge Graph self-updates, saving hundreds of hours of manual tagging, while improving accuracy
Accrete’s semantically unified graph enables its army of agents to reason, act, and automate tasks
Accrete’s army of agents combine the power of a unified graph and expert agent sub-graphs to capture tacit knowledge
Frequently asked questions
What is a Knowledge Engine?
A Knowledge Engine is an AI platform that captures human expertise, ingests and semantically unifies data from many sources, stores everything in a self-updating knowledge graph, and layers reasoning algorithms on top so it can learn continuously, remember long-term, and generate new, trustworthy insights or decisions-think of it as a domain-specific “digital brain” that sits between raw data or an LLM interface and the real-world actions you want to automate, going far beyond a search engine’s document retrieval or a stand-alone LLM’s pattern-matched text generation by providing persistent memory, explicit grounding, and expert-level decision automation already powering use cases like supply-chain influence detection, IT change-risk mitigation, and narrative monitoring.
What does a Knowledge Engine do?
A Knowledge Engine continuously captures tacit human expertise, ingests data of every modality and schema, automatically extracts, normalizes and links that information into a self-updating knowledge graph, then equips AI agents with reasoning functions that let them traverse this persistent memory to create new knowledge and deliver recommendations or actions-so complex decisions that once took armies of analysts can now be made in seconds with super-human accuracy.
How is a Knowledge Engine different from an LLM?
A Knowledge Engine is an always-learning, domain-specific “digital brain” built around an explicit, self-updating knowledge graph that fuses real-time data with captured expert judgment, whereas a large language model is a pattern-matching text generator that relies on static pre-training and lacks persistent memory or grounding; the Knowledge Engine can therefore store and refine facts over time, explain its reasoning, and drive automated decisions or actions, while an LLM, on its own, merely produces plausible text without guaranteed accuracy, traceability, or the ability to continuously ingest new signals and apply them with domain-aware logic.
Apply for early access:
Experience the future of decision automation with Accrete's Knowledge Engines. Limited availability.
Thank you for your interest, we'll be in touch soon.