Infrastructure Depth Report // Series 031
Graph Database vs.
Knowledge Graph vs.
GraphRAG vs.
Context Graph
Lead Author
Vivek Khandelwal
Co-Founder & CBO, CogniSwitch
Reading Time
~13 min read
If you're evaluating "graph AI" options: a graph database is storage infrastructure; it doesn't reason. A knowledge graph adds semantic structure and can reason deterministically. GraphRAG improves retrieval but still relies on an LLM to synthesize conclusions, which means you inherit all of the LLM's consistency and auditability problems. A context graph (our term for a governance-layer knowledge graph) removes the LLM from the reasoning path entirely: retrieval is deterministic, like SQL. If you're in a regulated industry and need to answer "what authorized this decision?", that's the distinction that matters.
Structural Overview
The architecture stack
Context Graph
Knowledge Graph
Graph Database
GraphRAG
Sits alongside, not above. Does not add a governance layer.
The confusion in the market: GraphRAG is treated as a progression from Knowledge Graph. It is not. It is a different axis entirely.
Enterprise AI is failing at the data layer, not the model layer.
Last year, a VC analyst put us in a slide deck next to Neo4j. His notes said "similar capabilities, different pricing." I spent the next fifteen minutes on that call explaining why they belong in completely different rows of a comparison table: one is storage infrastructure, the other is a governance architecture. That conversation is what this guide is trying to resolve in writing.
We have spent the last three years obsessing over the wrong layer of the stack. The industry has poured billions into model training, fine-tuning, and increasingly complex prompt engineering "tricks" to coax reliability out of probabilistic engines. But a probabilistic engine is, by definition, never deterministic. You cannot prompt your way out of a structural architecture flaw.
Most AI failures in the enterprise are not failures of the model to follow instructions; they are failures of the data structure to provide a verifiable foundation for reasoning. When a retrieval system returns a "similar" chunk of text that happens to be from a deprecated policy, the model has no way of knowing it's wrong. It synthesizes a perfect-looking, authoritative response based on flawed context. This is not a "hallucination" in the traditional sense. It's a governance failure.
This guide exists to solve an honesty problem. Terms like "Knowledge Graph" and "GraphRAG" are being used interchangeably by marketing departments, but they represent fundamentally different levels of technical maturity and risk. To introduce the "Context Graph" (a term we coined to describe a governance-first knowledge architecture), we must first earn the right by providing the most clinical, fair-minded teardown of the current graph landscape.
The Intellectual Brutalist Approach
We do not believe in "it depends." Every architecture has a traceability ceiling: the maximum level of consistency and auditability it can achieve regardless of implementation quality. If you are building for compliance, you need to know exactly where that ceiling sits for your chosen stack.
Key Objectives
- Untangle infrastructure from architecture
- Expose the traceability ceiling of RAG
- Define the role of deterministic reasoning
- Map compliance requirements to data structures
What is a graph database?
Primary function: Storage & Traversal
A graph database is infrastructure. At its most basic level, it is a storage engine optimized for managing highly connected data points. Instead of the rows and columns of a relational database (SQL), it stores information as nodes (entities) and edges (relationships).
Think of it as the substrate. Neo4j, Amazon Neptune, Memgraph, and ArangoDB are world-class at one thing: high-performance traversal. They can answer "what is connected to what?" across millions of hops in milliseconds. But here is the critical distinction: a graph database is agnostic about domain meaning. You can connect a "Patient" to a "Symptom" with a relationship called "HAS_COFFEE_WITH" or "TREATED_FOR," and the database won't blink. It doesn't know what those relationships mean for your business logic or your compliance requirements.
The tell-tale sign of a pure graph database implementation is the absence of an ontology. It has a schema (it knows what properties a node can have), but that schema doesn't encode domain semantics. If you ask a graph database "which clinical protocol governed this decision?", it cannot answer. It can tell you which document node is linked to which decision node, but it has no understanding of the hierarchy of authority, the validity dates of the protocol, or the logic required to interpret the connection.
"Documents are good for human beings but not so good for AI, especially an AI which is a probabilistic engine."
— Dilip Ittyera, CTO, CogniSwitch
For AI teams, a graph database is the filing cabinet. It is the physical (or virtual) location where the data lives. It is not the filing system, and it certainly isn't the auditor. Comparing a graph database to GraphRAG is like comparing a steel mill to a car engine: one is the raw material infrastructure, the other is an assembly of components designed for a specific function. We see enterprise teams make this category error constantly, evaluating Neo4j as an alternative to a RAG pipeline. They are not alternatives; they sit in different layers of the stack.
What is a knowledge graph?
Primary function: Meaning & Relationship
A knowledge graph is a graph that has been given meaning. While a graph database provides the structural capability to connect nodes, a knowledge graph provides the intellectual framework that defines what those connections signify.
In a knowledge graph, entities have types (e.g., this is a "Legal Clause," not just a text string) and relationships have formal, machine-readable definitions. The entire system is built according to an ontology, a schema that encodes domain semantics. This means the system "knows" that a "Sub-policy" inherits the constraints of its "Parent Policy," or that "Prior Authorization" is a process that must precede "Claim Submission."
A knowledge graph is an intellectual artifact, not just a data store. Building one requires ontology design: domain experts (not just data engineers) deciding what concepts exist, how they relate, and what inference rules apply. You can build a knowledge graph using Neo4j or Amazon Neptune as the storage layer, but the "knowledge" part comes from the metadata layer you build above it.
ENTITY TYPES
Entities are not just labels; they are classes with defined properties and inheritance rules.
RELATIONSHIP SEMANTICS
Edges have properties like symmetry, transitivity, and cardinality constraints.
FORMAL ONTOLOGY
The schema is a formal model of the domain knowledge, often using standards like RDF/OWL.
DETERMINISTIC RETRIEVAL
Queries use formal languages like SPARQL or Cypher to retrieve exact matches based on logic.
The fundamental limitation of the standard Knowledge Graph in an AI context is that it often still hands its retrieved results to an LLM for the final synthesis. This is where the "traceability ceiling" appears. While the retrieval step is deterministic (you can show exactly which entities were pulled), the reasoning step (where the LLM explains why those entities lead to a specific conclusion) remains a "black box." In a KG + LLM architecture, the LLM is still the final interpreter, and its interpretation can vary from one call to the next.
What is GraphRAG?
Primary function: Retrieval Enhancement
A GraphRAG is a retrieval architecture pattern: a pipeline that uses a graph structure to improve what gets handed to the LLM. In a typical GraphRAG implementation (like the Microsoft reference architecture), a large corpus of unstructured text is broken into chunks. An LLM is used to identify entities and relationships within those chunks, which are then stored in a graph structure.
When a query comes in, the system doesn't just do a vector search for "similar" chunks. Instead, it traverses the graph to find connected entities and context that might be several "hops" away from the original query. This retrieved sub-graph is then fed into the prompt as the context for the LLM to synthesize the final response.
GraphRAG is a massive improvement over standard vector RAG. By following explicit relationships, it can solve "multi-hop" queries that similarity search misses. It provides better grounding and reduces (but does not eliminate) the hallucination rate. However, for regulated industries, GraphRAG has a structural flaw: the graph itself is often auto-generated and opaque.
The Real-World Verdict
"If it works, it works, otherwise it doesn't. So it's that sort of issue. You were getting good results but it takes so much longer to create that graph and we don't have control over those graphs."
— Shrikant Gangal, Technical Evaluator (Financial Services)
The problem Shrikant identifies is the "80/20 Trap." GraphRAG gets you to 80% accuracy very quickly. But because the graph was built by a probabilistic engine (the LLM used during indexing), there is no easy mechanism to fix the 20% of cases that fail. You cannot "tune" an auto-generated graph the way you can tune an ontology. More importantly, you cannot prove why the synthesis LLM combined those specific graph nodes into its conclusion. You can show what was retrieved, but you cannot audit the reasoning chain. For a compliance officer, "mostly correct" is just a faster way to reach a failed audit.
Where the LLM sits in each pipeline
GraphRAG
In the critical reasoning path
Ceiling: You can show what was retrieved. You cannot prove why the model concluded what it did.
Context Graph
Output-only — not in reasoning path
Full audit trail: document → section → concept → decision. Every hop is traceable.
So what is a context graph? (And why is it a coined term)
Primary function: Audit & Determinism
A context graph is a knowledge graph designed specifically for AI governance in regulated industries.We must be explicit: “context graph” is not an industry-standard term. We are naming something that didn't have a name yet, a knowledge architecture where the LLM is removed from the retrieval and reasoning path entirely.
In a context graph, the AI doesn't "guess" at the answer by looking at similar text. Instead, the domain knowledge (compliance policies and SOPs) is encoded directly into the graph as logic. When a query is made, the system traverses this deterministic structure to pull out data points and decision paths. Think of it like SQL: the database doesn't guess at your query; it executes it according to formal rules.
"There is no AI involved in retrieval. You are not throwing chunks of text at a probabilistic engine. You are traversing a deterministic structure to pull out data points, which are then returned in structured form."
Dilip Ittyera
Chief Technology Officer
A context graph adds three critical layers that separate it from a standard knowledge graph or a RAG pipeline:
01 // Policy Encoding
Standard Operating Procedures (SOPs) and compliance rules are not just "available" as documents. They are compiled into formal logic. The graph doesn't just store the policy text; it stores the structure of the policy's decision tree.
02 // Provenance and Versioning
A context graph captures which exact version of a policy governed which decision. In regulated industries, policies change. An auditor doesn't want to know what the current policy says; they want to know what the policy said on the day the decision was made. A context graph provides a complete audit trail from document → section → concept → decision.
03 // Agent Interaction Traces
The reasoning path is recorded as a series of deterministic steps, not an opaque LLM synthesis. You can query the system after the fact to see exactly which logic gates were passed. This replaces the unreliable "chain of thought" explanation with a verifiable execution log.
This is the direct answer to the "traceability ceiling." By moving the reasoning from the probabilistic LLM to the deterministic context graph, we achieve a level of defensibility that is required by law in healthcare, finance, and government. The LLM's role is relegated to what it does best: formatting the final output for human readability. The "truth" of the answer is established before the LLM ever sees it.
The decision matrix
The right architecture depends on the cost of a wrong answer in your domain.
Graph Database
Use when you need high-performance traversal of connected data (e.g., social networks, recommendation engines, fraud detection patterns) and don't need semantic reasoning or domain logic.
The Limit
Structural connection only. The database is agnostic to the meaning of the data.
Knowledge Graph
Use when your domain has well-defined entities and you need structured retrieval over them. Ideal for catalog management, research discovery, and enterprise data integration.
The Limit
If using an LLM for final synthesis, you still face an interpretation and traceability ceiling.
GraphRAG
Use for large unstructured document corpora needing multi-hop context retrieval. Best for exploratory research, customer support bots, and internal knowledge search.
The Limit
Acceptable for exploratory use. Not acceptable for high-stakes decisions requiring audit trails.
Context Graph
Use in regulated industries (Healthcare, Finance, Legal) where outputs must be defensible to an auditor. Required when you need to answer "what authorized this specific decision?"
The Advantage
Deterministic reasoning with a full provenance trail.
The regulatory imperative
Risk mitigation at the architecture level
"For regulated industries, the compliance team's first question is always: 'Do you use LLMs? Which ones?' A context graph architecture answers this cleanly: LLMs are used for formatting only."
Healthcare // Prior Authorization
A prior authorization AI needs to tell an auditor not just "we checked the payer policy" but exactly which version of the policy was active at the time of the claim, which specific clause triggered the denial, and whether that policy had been superseded by a more recent update. Vector RAG and GraphRAG fail here because they rely on the model's interpretation of a document chunk. A context graph succeeds because the policy is encoded as logic. The system doesn't "read" the policy; it "executes" it.
Financial Services // Credit Risk & FIS Evaluation
In financial services, a credit decision AI needs to show regulators a complete reasoning chain, not a similarity-matched document. Shrikant Gangal at FIS, after evaluating multiple graph approaches, noted that while simple RAG and GraphRAG can return "good results," they lack the control and deterministic nature required for risk management. A context graph provides the human-in-the-loop knobs to tune the 20% of complex cases that standard RAG architectures consistently miss.
Architectural Ceiling Analysis
Every architecture has a traceability ceiling.
Know where yours sits before you commit to a production deployment.
Graph Database
Consistency
Traceability
Compliance-ready
Knowledge Graph
Consistency
Traceability
Compliance-ready
GraphRAG
Consistency
Traceability
Compliance-ready
Ceiling is structural. Better engineering does not raise it.
Context Graph
Consistency
Traceability
Compliance-ready
The technical comparison
Infrastructure // Semantics // Architecture // Governance
| Metric | Graph Database | Knowledge Graph | GraphRAG | Context Graph |
|---|---|---|---|---|
| What it is | Storage infrastructure | Semantically structured data | Retrieval architecture pattern | Governed knowledge infrastructure |
| Primary unit | Nodes and edges | Typed entities + defined relationships | Document chunks as nodes | Policy rules + typed entities + agent traces |
| Answers | What is connected? | What does this relationship mean? | What context is relevant? | What authorized this decision? |
| Built with | Cypher, Gremlin | Ontology (OWL, RDF) | Chunking + LLM Indexing | SOP Encoding + Audit Layer |
| LLM role | None | Synthesizes from entities | Synthesizes from context | Output formatting only |
| Consistency | N/A | Middle | Low-Middle | High (Deterministic) |
| Traceability | Structural only | Retrieved Entity Trail | Traversed Node Trail | Full Provenance Trail |
| Control | Full (Schema) | Full (Ontology) | Low (Auto-generated) | Full (Curated) |
| Compliance | No | Partial | No | Yes |
| Examples | Neo4j, Neptune | Ontotext, Stardog | Microsoft GraphRAG | CogniSwitch Clarity |
Practitioner FAQ
Skeptical questions from technical evaluators who have been burned by "magic" AI solutions.
Q1Is Neo4j a knowledge graph?
No. Neo4j is a graph database, infrastructure for storing and querying connected data. A knowledge graph requires ontology design that gives semantic meaning to the relationships. You can build a knowledge graph using Neo4j as the storage backend, but Neo4j alone is not a knowledge graph. Conflating the two is how teams end up with graph-shaped RAG that still fails their compliance audit.
Q2Is a knowledge graph the same as GraphRAG?
No. GraphRAG uses a graph structure to improve retrieval for LLMs: it's a retrieval architecture pattern. A knowledge graph is a data representation standard with formally defined semantics. A knowledge graph delivers answers through formal logic; GraphRAG routes output through LLM synthesis. Most GraphRAG implementations use a graph database with document chunks as nodes, not a formally ontology-governed knowledge graph.
Q3What makes a context graph different from a knowledge graph?
A context graph removes the LLM from the retrieval and reasoning path. Retrieval is deterministic: think traversal like SQL rather than semantic similarity matching. A context graph also adds: policy encoding (SOPs compiled into logic), provenance tracking (which policy version governed which decision, with full document/section/concept trail), and agent interaction traces. Auditability is the real requirement, not just retrieval.
Q4Isn't GraphRAG good enough for most enterprise use cases?
For exploratory, low-stakes use cases, yes. For regulated industries, no. GraphRAG gets you good results on 70-80% of queries. But the graph is auto-generated and there are no knobs for the 20% that fails. Technical evaluators who have deployed GraphRAG in financial services production describe it plainly: "If it works, it works, otherwise it doesn't." That's acceptable for internal search. Not for prior authorization decisions or credit risk assessments.
Q5Can I use Neo4j as part of a CogniSwitch deployment?
Yes. CogniSwitch's context graph layer sits above a graph database (Neo4j or others). CogniSwitch adds the ontology, the policy encoding, the curation pipeline, and the audit trail. The storage layer is your choice.
Q6Does CogniSwitch replace my vector RAG system?
No. Vector RAG retrieves semantically similar text chunks, fast and effective for unstructured document search. A context graph retrieves policy-governed, provenance-tracked context for compliance decisions. CogniSwitch also uses vectors, but for related concept matching rather than chunk retrieval. Most regulated deployments need both layers.
Q7Is GraphRAG enough for healthcare AI compliance?
No. GraphRAG improves retrieval accuracy but cannot address compliance: it cannot tell an auditor which SOP version governed a decision, whether that SOP was current at the time, or why a specific output was blocked or approved. "We retrieved these document nodes and the model synthesized this conclusion" is not an auditable answer. Compliance requires a deterministic reasoning chain.
Q8Do you use LLMs? Is that a problem for compliance?
CogniSwitch uses LLMs for output formatting only, not for retrieval or reasoning. The knowledge structure is queried deterministically. This makes the architecture model-agnostic: Azure OpenAI, hosted Llama, Gemini. It's fully compatible with "no data leaves our cloud subscription" requirements. The deterministic layer is unaffected by model choice.
Q9Is this a black box? How do we fix the cases that don't work?
No. Every element of the context graph is inspectable: nodes, edges, properties, rules, and the full provenance trail (document → section → concept). Human-in-the-loop curation means subject matter experts can review and tune any part of the graph. This is the direct answer to the 20% problem: when a case fails, you can identify exactly where in the graph structure the failure occurred and fix it. Auto-generated GraphRAG graphs give you no such entry point.
Ready for deterministic AI?
Stop patching a probabilistic stack and start building on a governance-first architecture. If your AI outputs need to be defensible, you need a context graph.