Enterprise RAG
Retrieval-augmented generation grounded in your ontology, knowledge bases, and lakehouse vector search — so agents answer from your verified data with citations and provenance, low hallucination, entirely inside your perimeter.
Grounded in Your Ontology
Retrieval runs over your ontology, knowledge bases, and lakehouse vector search — so answers come from verified business meaning, not generic model memory.
Citations & Provenance
Every answer traces back to the source documents and data it was drawn from, keeping AI auditable and low-hallucination.
Inside Your Perimeter
Embeddings, retrieval, and generation run sovereignly within your perimeter — no data sent to external services.
Enterprise RAG (retrieval-augmented generation) is the technique of grounding AI answers in an organisation's own data — retrieving relevant facts from its knowledge bases, ontology, and lakehouse before the model responds. On the AI OS, enterprise RAG is sovereign: retrieval runs over your ontology and vector search inside your own perimeter, every answer carries citations and provenance, and outputs are governed, so agents answer from verified data with low hallucination rather than from generic model memory.
Large language models are fluent but ungrounded: ask them about your business and they guess. Enterprise RAG fixes that by retrieving the right facts from your own data first, then letting the model answer from them. Scrydon makes RAG sovereign and trustworthy: retrieval is grounded in the ontology and the Cognitive Enterprise, vector search runs in the lakehouse inside your perimeter, and every answer is traceable to its sources. The result is AI that answers from your verified data — with citations, provenance, and far less hallucination — without anything leaving your control.
Enterprise RAG in the Scrydon platform
One integrated, sovereign architecture. Here is where Enterprise RAG sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents to enable your processes
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Link your processes, knowledge & data to ontologies.
Unified storage, structured compute, and secure multi-modal data processing.
Autonomous operatives with specialised skills executing tasks across systems.
Sovereign pipelines, federated APIs, and seamless connector meshes.
Secure domain federation, trusted data sharing, and cross-boundary intelligence.
Enterprise RAG in depth
Human + AI Orchestration
The AI OS for Humans & AI Agents to enable your processes
The Human + AI Orchestrator is the operational runtime at the heart of the AI OS — scheduling, routing, and governing every task across your enterprise, whether executed by an AI agent, an existing system, or a human.
Most organisations have broken processes: encoded in siloed systems or locked in people's heads. The AI OS makes them visible and executable. It captures intent, synthesises context, acts — then feeds every result back into the ontology so the next run is smarter. All of it inside your perimeter.
Cognitive Enterprise
Link your processes, knowledge & data to ontologies.
Most organisations have data they can't use — not because it doesn't exist, but because nothing connects it. The Cognitive Enterprise layer is the defining intelligence of the AI OS: a living, queryable semantic model of your organisation's entities, processes, and rules. It is the single source of truth that allows every agent, analyst, and workflow to reason about your business with a consistent understanding.
Without it, AI agents reason on noise. With it, they reason on the business.
- Entity graph: Model customers, accounts, orders, products, and any domain concept — then connect them with typed, traversable relationships.
- Process integration: Link real-world workflows to ontology entities so agents understand how data flows through your business.
- Continuous enrichment: Agents automatically enrich ontology nodes with fresh data from the lakehouse, keeping the model current without manual effort.
Answers grounded in your verified data
Enterprise RAG retrieves before it generates. When an agent or user asks a question, the platform finds the most relevant facts from your knowledge bases, ontology, and lakehouse, then the model answers from those facts — with the sources attached. Because retrieval is grounded in the ontology rather than loose documents, agents pull the right entities and definitions every time.
Vector search in the lakehouse — Embeddings are stored and queried alongside your data in the lakehouse — no separate, ungoverned vector database.
Ontology-grounded retrieval — Retrieval traverses the ontology and Cognitive Enterprise, so agents fetch the right entities, relationships, and definitions.
Citations and provenance — Every answer carries links back to the source documents and data, so reasoning stays auditable.
Governed and sovereign — DLP screens outputs, access is scoped per identity, and the whole pipeline runs inside your perimeter.
Why ontology-grounded RAG beats classical RAG
Classical RAG retrieves text chunks by vector similarity, so it tends to return partial or loosely related passages, misses how facts connect, cannot follow relationships across the data, and is prone to hallucination with weak provenance. Scrydon grounds retrieval in the Cognitive Enterprise — the connected ontology, knowledge bases, and lakehouse — so an agent retrieves the right entities and the relationships between them, answers in business terms, and traces every answer back to defined concepts and their sources. The result is markedly higher precision, fewer hallucinations, and explainable, governed answers.
Retrieval over meaning, not just text — Queries run against the ontology and its typed relationships, not similarity-matched chunks of text.
The right context, every time — Retrieval pulls the connected entities a question actually needs, instead of partial or off-topic passages.
Multi-hop reasoning — Agents traverse relationships across the model that classical, chunk-based RAG simply cannot follow.
Cited and explainable — Every answer traces to defined concepts and their source data, rather than an opaque vector match.
Lower hallucination, governed by policy — Grounding in verified business data and access controls keeps answers accurate and permitted.
From confident guessing to verifiable answers
A model on its own will answer your questions confidently and sometimes wrongly, with no way to check where the answer came from. Enterprise RAG turns that into verifiable, grounded answers: retrieval anchors every response in your own data, citations make it checkable, and provenance makes it auditable. Grounding retrieval in the ontology — rather than a pile of documents — is what sharply reduces hallucination, because every answer traces back to a defined concept in your verified data.
Frequently asked questions
What is enterprise RAG?+
How does RAG reduce hallucination?+
Where does the retrieval data come from?+
Is enterprise RAG sovereign and secure?+
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