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The most common knowledge graph use cases are customer 360 and golden records, fraud and anti-money-laundering investigations, regulatory compliance, supply chain visibility, product data, clinical and regulatory data in life sciences, enterprise search, and grounding AI models and agents. Each has the same shape: the answer to an everyday question is spread across systems that name the same things differently.
A knowledge graph is a model of real-world entities, such as customers, accounts, suppliers, products and drugs, and the typed relationships between them, stored as data and described by a shared vocabulary called an ontology.
Each fact is a small statement of subject, relationship and object: “Acme Ltd supplies Part 4471.” “Part 4471 is a component of Product X.” Because relationships are stored as data rather than rebuilt in every query, software can follow them from one system’s records into another’s, and an AI model can read what each thing means instead of guessing from column names.
For the full explainer, read What Is a Knowledge Graph? The Complete Guide. For how one is built and governed at enterprise scale, see our enterprise knowledge graph page.
Two facts, one graph
A knowledge graph earns its place when three things are true: the same entity has different IDs in different systems, the question crosses those systems, and someone has to defend the answer.
Knowledge graph use cases are the jobs a connected, governed model of your business does better than tables alone: resolving who is who, following relationships across systems, and giving people and AI answers they can trace. These are the enterprise knowledge graph use cases we see most.
Resolve every record that describes the same customer or household into one governed profile, with lineage to each source, so every team and every AI model starts from the same customer.
“Which households hold both a mortgage and a business account with us?”
Link the accounts, people, addresses, devices and counterparties that belong to the same actor, so investigators see a network instead of isolated rows.
“Which accounts share an address or phone number with this flagged customer?”
Report from data that carries its own lineage and history, so every figure traces back to its source and you can show what the data said on the filing date.
“Where did this reported number come from, and what did we know on March 31?”
Connect suppliers, sites, parts, lots and shipments across ERP, PIM and partner data, so impact analysis is a query instead of a week of spreadsheets.
“Which finished products depend on a site that just went offline?”
Merge duplicate SKUs from retailers, distributors and internal systems into golden product records, classified against a product ontology every channel shares.
“Is this the same product at three retailers, and which attributes are missing?”
Connect clinical, safety and manufacturing data under shared medical vocabularies, so questions run across studies and submissions and every answer traces to its source.
“Has this adverse event shown up in other studies under a different code?”
Search documents and records together by meaning, with permissions enforced on every result, so people get the answer rather than a list of files.
“What do our contracts with this customer say about data residency?”
Retrieve facts and their relationships from the graph to ground a language model, so answers cite the records they came from instead of text that only looks similar.
“Which customers does this contract clause cover, and where does it say so?”
Define business terms once in an ontology, so “active customer” or “net revenue” means the same thing in every dashboard, notebook and AI assistant.
“Does “active customer” mean the same thing to finance and marketing?”
Give agents context from a graph that enforces access policy on every fact and keeps every change, so you can see what an agent knew and what it was allowed to see.
“What could this agent see when it made last Tuesday’s recommendation?”
All of these run on the same foundation, an enterprise knowledge graph. Most teams start with one use case and add the next on the same ontology instead of starting over.
The heaviest users are industries where many systems describe the same entities and a regulator, auditor or partner asks where the answer came from: financial services, life sciences and healthcare, supply chain and CPG, insurance, government, and media.
Banks, insurers and capital markets firms use knowledge graphs to resolve customers and counterparties into single records, follow money and ownership across accounts during fraud and AML investigations, and report figures with lineage an examiner can follow.
Deposits live in the core, relationships in the CRM, exposure in the loan system, and research in documents and email. The same customer appears under different identifiers in each. Every question that crosses those systems, from “who is in this household?” to “where did this reported figure come from?”, starts with manual reconciliation.
A financial knowledge graph models the parties and the money between them. Many teams borrow terms from FIBO, the Financial Industry Business Ontology, which the EDM Council hosts and develops in OWL. Fluree is built on the same W3C standards (RDF, OWL and SHACL), so those terms can sit in your graph’s ontology next to your own.
Customer proof · Community bank
A community bank used Fluree to build golden records for more than 330,000 customers, resolving household relationships across identifiers and unifying 3–5 competing reporting systems into one source of truth.
Read the community bank case studyCustomer proof · Global financial services leader
A global financial services leader automated tagging of its research documents and emails against its own ontology and taxonomies, and grew its knowledge base tenfold through continuous topic discovery.
Read the data portal case studyFraud rarely sits in one record. A knowledge graph links the accounts, people, devices, addresses and counterparties behind each transaction, so a ring of related applications or a chain of transfers shows up as connected entities instead of unrelated rows.
Entity resolution decides that “J. Smith”, “John Smith” and the company John Smith owns are connected, and keeps the evidence for each match.
Investigators ask graph questions such as “which accounts are within two transfers of this one?” or “who else uses this device?”, patterns that row-by-row screening misses.
Every fact keeps its source and its history, so you can show what the bank knew, and when, for each decision on an alert.
Fluree provides the resolved, governed graph and its history. Transaction monitoring, scoring models and case management keep running in the tools you already use, and can query the graph for context. Insurers apply the same pattern to claims fraud, linking claimants, providers, vehicles and addresses: see knowledge graphs for insurance.
Supply chain teams use a knowledge graph to connect suppliers, sites, parts, products, lots and shipments across ERP, PIM and partner data, so “what is exposed if this supplier fails?” becomes a query and traceability evidence is ready before anyone asks for it.
The records that matter sit with suppliers, plants, carriers and distributors, in systems no single company controls. The same supplier is spelled three ways across procurement, finance and quality. Certificates and supplier notices arrive as PDFs. That is why impact analysis is the classic knowledge graph supply chain use case: mapping a disruption today means stitching those sources together by hand.
A supply chain knowledge graph models the network itself: who supplies what, from where, into which products, and with what evidence. Events can arrive in a standard form: GS1’s EPCIS 2.0 standard (ratified June 2022) defines JSON and JSON-LD bindings for supply chain events, which record what, when, where, why and how. JSON-LD is the format FlureeDB stores natively.
Regulators are shortening the deadline for that evidence. The FDA’s Food Traceability Rule requires covered companies to keep key data elements for critical tracking events and to provide records within 24 hours of an FDA request. As of October 2026, the FDA says it will not enforce the rule before July 20, 2028.
Customer proof · Global 500 CPG company
A Global 500 CPG company used Fluree to unify five product data sources, found that 48% of apparently unique records were duplicates, and reached 97.5% catalog accuracy in three weeks. The resolved product data supports forecasting, inventory and fulfillment planning across brands.
Read the CPG case studyHealthcare and life sciences teams use knowledge graphs to connect clinical, safety, regulatory and manufacturing data under shared vocabularies such as MedDRA, SNOMED CT and CDISC terminology, so questions run across studies and systems and every answer traces to its source.
Each clinical trial captures data in its own schema and vocabulary, so little of what one study learns carries cleanly into the next. Regulatory evidence is spread across LIMS, ERP, regulatory information systems and document repositories. When a reviewer asks where a number came from, teams rebuild the trail by hand.
A clinical knowledge graph resolves the people and things that recur across studies, such as investigators, sites, subjects and drugs, and codes them against standard vocabularies. On the care side, HL7’s FHIR R5 specification defines an RDF representation of FHIR resources in Turtle (a trial-use part of the standard), so patient and clinical records can join a graph in a standard form. The same model extends to provider and payer data such as encounters and claims.
Customer proof · $2B+ pharmaceutical company
A $2B+ pharmaceutical company used Fluree to classify and clean customer, product and order data while consolidating more than 10 warehouses onto one cloud platform. That clean, consistent data is now the base for its drug, supply chain and commercial AI applications.
Read the pharma case studyA knowledge graph gives AI models and agents facts with explicit meaning, resolved identities and access rules attached, so answers are grounded in your data and traceable, rather than guessed from text that looks similar.
In Fluree’s April 2024 study, GraphRAG for GenAI Accuracy, relational RAG started at 8–15% zero-shot while both graph architectures started at 60–65%. With the data enriched and joined, relational topped out at 80%, a centralized graph at 80–90%, and a decentralized graph at 95%+.
The lesson holds for every use case on this page: the work you put into the data, resolving entities and joining sources, shows up as accuracy.
More on this in Why AI Needs a Knowledge Graph and our guide to AI-ready data.
Pick a question people ask every week that crosses at least two systems, involves one entity those systems identify differently, and has someone who must defend the answer. Start narrow, then reuse the model.
If one database already answers it, you don’t need a graph for it. Look for the question people ask every week that needs two or more systems.
Customer, supplier, product or patient: the entity whose records disagree across systems is where the graph pays off first.
An examiner, auditor, regulator or customer turns lineage and history from nice to have into a requirement.
One domain and a handful of sources reaches production in weeks. The ontology you write carries into the next use case.
The ontology outlasts the project. In the CPG catalog work above, the product category framework, attribute definitions and equivalence rules became a governed, versioned vocabulary that new sources, new analytics and new AI agents classify against.
Fluree AI is the context layer for enterprise AI: one governed knowledge graph your people and agents can trust. Each use case on this page adds to the same graph instead of starting a new project.
Fluree is the complete context layer: meaning, entities, policy, lineage and time in one system. Already run a catalog? Fluree reads from it. You don’t need one to start.
Fluree reads source systems where they live, and lake tables copied into Fluree refresh automatically. Documents become graph facts that cite the passage they came from. Policy is enforced inside the database on every query, and you can query the graph as of any past moment.
Apps query in SPARQL, openCypher, GraphQL or JSON-LD, and agents connect over MCP. Everything is built on W3C standards: RDF, JSON-LD, SHACL, OWL and SKOS.
Lakehouse tables and SQL sources, queried where they live and resolved into golden records with lineage.
PDFs and Office files become entities and relationships, each one kept with the sentence that proves it.
Taxonomies and ontologies, reviewed and versioned like a release, then published to every capability.
The knowledge graph database underneath: policy on every fact, full history and built-in search, in SPARQL, openCypher, GraphQL or JSON-LD.
FlureeDB is source-available under BUSL 1.1; each release converts to Apache 2.0 after four years.
Short answers to the questions teams ask when they scope a first knowledge graph.
Knowledge graph use cases are the business problems a connected model of entities and relationships solves better than tables alone. In the enterprise, the most common are customer 360 and golden records, fraud and AML investigations, regulatory compliance, supply chain visibility, product data management, clinical and regulatory data in life sciences, enterprise search, and grounding AI models and agents. Outside the enterprise, the best-known applications are web search and recommendation engines.
Financial services, life sciences and healthcare, supply chain and consumer goods, insurance, government, and media lead the way, because each runs many systems that describe the same entities and must show where its answers came from. Banks use knowledge graphs for customer and fraud work, pharmaceutical companies for clinical and regulatory data, and manufacturers and CPG companies for supplier and product data.
Financial institutions use knowledge graphs to resolve customers, households and counterparties into golden records, to follow payments and ownership during fraud and AML investigations, and to produce regulatory reports where each figure traces to its source. Many financial knowledge graphs align their terms with FIBO, the Financial Industry Business Ontology, which the EDM Council hosts and develops in OWL.
Fraud usually spans several records. A knowledge graph resolves the people, accounts, devices and addresses behind transactions into connected entities, so investigators can see rings and chains that row-by-row rules miss, and can show what was known at the time of each decision.
Supply chain knowledge graphs connect suppliers, sites, parts, products, lots and shipments across ERP, PIM and partner data. Teams use them to trace the impact of a disruption, see risk beyond tier-one suppliers, master supplier and product records, and keep traceability evidence linked to its source.
In healthcare and life sciences, knowledge graphs connect clinical, safety, regulatory and manufacturing data under standard vocabularies such as MedDRA, SNOMED CT and CDISC terminology. Teams use them to review safety signals across studies, assemble submissions from connected data, and trace every answer to its source. On the care side, HL7 FHIR R5 defines an RDF representation of FHIR resources, so clinical records can join a graph in a standard form.
Knowledge graphs give AI models and agents facts with explicit meaning, resolved identities and access rules. Retrieval can follow relationships and cite its sources, an approach called GraphRAG. In Fluree’s April 2024 study, GraphRAG for GenAI Accuracy, relational RAG started at 8–15% zero-shot while both knowledge graph architectures started at 60–65%.
A government department unified more than 6 million species records and decades of field content, including camera trap images, audio and scanned reports, into one governed knowledge graph. It now serves more than 60 applications and answers natural-language questions through GraphRAG. Read the biodiversity case study.
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