Three approaches to grounding AI in private enterprise data — what each does, where each is the right fit, and how to choose.
Most organizations now have a foundation model and a modern data platform. The challenge is no longer access to data but it’s how to reliably ground AI in it.
RAG, GraphRAG, and GraphAI are the three approaches that matter most for enterprise AI grounding. They are not competing alternatives as they are suited to different classes of questions. Understanding the distinction determines whether your AI delivers answers that are reliable enough to act on.
This guide explains how each approach works, where it is the right fit, and where it reaches its limits. The goal is a clear framework for matching approach to use case, not a prescription to choose one over the others.
|
Approach |
In one sentence |
|
RAG |
Retrieves similar document chunks by vector search and synthesizes an answer using an LLM. |
|
GraphRAG |
Traverses entity relationships in a knowledge graph to retrieve richer, cross-domain context before LLM synthesis. |
|
GraphAI |
Matches a question to a validated ontology query, executes it deterministically as SQL, and returns a fully traceable answer. |
THE THREE APPROACHES
How each one works and where each one fits
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01 Retrieval-Augmented Generation (RAG) Document grounding — practical and well-supported for factual recall from unstructured content. How it works Enterprise documents are chunked and encoded as vector embeddings. At query time, the question is encoded and the most similar chunks are retrieved. Those chunks are passed to an LLM as context, which synthesizes an answer. Where it fits RAG is well suited to document-centric questions: policies, contracts, technical manuals, and knowledge bases. It reduces hallucinations compared to an ungrounded model and is practical to implement on existing document stores. Its limits RAG retrieves by similarity, not by meaning. It cannot traverse entity relationships, determine whether retrieved content is authoritative, or reliably answer questions that connect multiple operational domains. The LLM synthesis step is probabilistic — even with strong retrieved context, the model can produce answers that are inconsistent with the source. For regulated or high-stakes decisions, this uncertainty matters. |
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02 Graph-based RAG (GraphRAG) Relationship-aware retrieval — suited to operational questions that cross business domains. How it works GraphRAG extends retrieval by following explicit entity relationships in a knowledge graph. Rather than matching text by similarity, it traverses connections — asset certifications, maintenance histories, contract hierarchies — to gather structured, domain-spanning context. That context is then passed to an LLM for synthesis. Where it fits GraphRAG is the right choice for questions that require connecting data across multiple systems: which assets are at risk, which engineers are qualified, and what the scheduling constraints are. It aligns retrieval to the semantic structure of the business, not to the physical layout of a database schema. Its limits The LLM synthesis step is still probabilistic. Retrieval is more precise because it is relationship-guided, but the model can still produce inconsistencies when graph context is large or spans ambiguous relationships. Answer quality is also directly determined by the quality of the underlying semantic model — a weak ontology produces weak GraphRAG answers. |
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03 GraphAI Deterministic, ontology-driven, SQL-executed — designed for questions where the answer must be validated before it is returned. How it works GraphAI is a distinct approach. The system generates vector embeddings for every valid semantic query defined in the ontology. A user’s question is matched to the closest validated query. That query is then executed deterministically as SQL against the knowledge graph, running directly on a governed Databricks compute. The answer includes full graphical lineage from response back to source entity. Where it fits GraphAI is suited to regulated operational decisions, cross-domain questions where an incorrect answer has material consequences, and agentic AI workflows where AI outputs drive real-world actions. The system only produces answers it can validate — that constraint is the point. Every answer is traceable to specific data, definitions, and graph traversals. Its scope GraphAI answers questions within the scope of the ontology. Questions outside that scope are not answered deterministically. Extending the coverage means extending the ontology — which domain experts can do using no-code modelling tools. This makes GraphAI a governed, auditable approach rather than a general-purpose one. |
THE COMPARISON
Head-to-head across five dimensions
|
Dimension |
RAG |
GraphRAG |
GraphAI |
|
Answer type |
Probabilistic — LLM synthesizes from retrieved context |
Probabilistic — graph retrieval is structured; synthesis step remains |
Deterministic — validated ontology query executed as SQL |
|
Cross-domain questions |
Limited — similarity retrieval; context may be incomplete |
Strong — relationship traversal spans business domains |
Strong — ontology defines relationships; traversal is explicit |
|
Explainability |
Partial — sources cited; synthesis path not traceable |
Moderate — retrieval path traceable; synthesis less so |
Full — lineage from answer to semantic query to source data |
|
Governance |
Depends on document or vector store access controls |
Inherits graph platform governance |
Runs within the Databricks Lakehouse; Unity Catalog controls apply throughout |
|
Suited for agentic AI |
Limited — answer uncertainty affects action reliability |
Good — richer context supports multi-step reasoning |
Strong — deterministic, traceable answers provide a reliable foundation for autonomous action |
THE DECISION GUIDE
Matching approach to use case
These approaches are not mutually exclusive. Most enterprise AI architectures use all three, routing each class of question to the mechanism best suited for it.
|
Use case |
RAG |
GraphRAG |
GraphAI |
|
Policy, contract, and knowledge base Q&A |
✓✓ Primary |
○ |
○ |
|
Technical manual and procedure retrieval |
✓✓ Primary |
✓ Enhances |
○ |
|
Cross-domain operational intelligence |
○ |
✓✓ Primary |
✓ Regulated |
|
Predictive maintenance (regulated assets) |
○ |
✓ Useful |
✓✓ Primary |
|
Supply chain disruption analysis |
○ |
✓✓ Primary |
✓ Governance |
|
Customer 360 and commercial intelligence |
○ |
✓✓ Primary |
✓ Governance |
|
Autonomous AI agents for operations |
○ |
✓ Limited |
✓✓ Primary |
|
Financial risk and exposure (regulated) |
○ |
✓ Useful |
✓✓ Primary |
|
AI copilot for analyst teams |
✓ For docs |
✓✓ Primary |
○ |
|
Across all three approaches, the quality of the semantic model is the primary determinant of answer quality. Ontology-aligned data improves RAG retrieval precision. Well-defined entity relationships enable more accurate GraphRAG traversal. Comprehensive ontology coverage determines how many questions GraphAI can answer deterministically. |
LOOKING AHEAD
What this means for agentic AI
Agentic AI — systems that take autonomous sequences of actions based on AI-generated outputs — raises the stakes for grounding approach selection significantly. When an agent acts on an AI answer, reliability directly affects the quality of real-world decisions: maintenance scheduling, procurement commitments, regulatory flags.
RAG-grounded agents inherit RAG’s probabilistic uncertainty. For information-gathering tasks this may be acceptable; for operational actions it typically is not. GraphAI provides the most reliable foundation for agentic AI: the system only produces answers it can validate, every answer is fully traceable, and agents can query the semantic graph programmatically via SDK with deterministic, governed results.
Implementing This on the Databricks Lakehouse
All three approaches can be implemented within a single architecture when built on a shared semantic knowledge graph. On the Databricks Lakehouse, this means a semantic model that runs within the Databricks Lakehouse on governed Delta tables, with Unity Catalog access controls applied throughout from raw data through to every AI answer.
|
Capability |
What it enables |
Approach |
|
Knowledge-Enhanced RAG |
Vectors generated from ontology-aligned data; stronger retrieval precision than raw document embeddings |
Enhanced RAG |
|
GraphRAG |
Semantic knowledge graph provides relationship-aware retrieval context across connected entities |
GraphRAG |
|
GraphAI (Episteme) |
Ontology-validated query matching; deterministic SQL execution; full graphical lineage per answer |
GraphAI |
|
Genie integration |
Fully contextualized Genie space backed by the semantic model; one line of code to deploy |
GraphRAG / GraphAI |
|
Agent SDK |
Governed semantic context exposed programmatically for use by autonomous AI agents |
GraphAI for agentic AI |
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If you’re evaluating how to ground AI across documents, data, and operations, it’s worth comparing these approaches in your own environment — on real questions, with real data. Kobai provides the semantic intelligence layer that makes all three approaches available on the Databricks Lakehouse, from a single governed semantic model. |
To explore further or discuss your specific use case, contact us at contact@kobai.io or visit kobai.io.