See Sense on your data.
Walk through your sources with a Fluree solutions architect.
Every system. One golden record.
Fluree Sense uses AI to classify, resolve, and semantically map your structured enterprise data — then continuously keeps it synchronized. No massive ETL projects. Just clean, linked, AI-ready data flowing into your knowledge graph.
Your data is everywhere. Your truth is nowhere.
Your enterprise data is scattered across dozens of systems — each with its own schema, terminology, and version of the truth. Customer in Salesforce, client in ERP, and account in billing may all describe the same entity, but no system understands that.
Fluree Sense uses AI to classify source data against a canonical model, resolve cross-system entities, and create semantic links that turn disconnected records into connected knowledge — without ripping out source systems.
The Sense Pipeline
From raw sources to knowledge graph in seven stages.
Plug into the systems you already run.
Sense ships connectors for the platforms enterprises already depend on — databases, SaaS apps, warehouses, files, APIs. Onboarding a new source is configuration, not engineering.
Databases
Oracle · PostgreSQL · MySQL · SQL Server
Enterprise apps
SAP · Salesforce · Workday · ServiceNow
Warehouses & lakes
Snowflake · Databricks · BigQuery · Iceberg
Files & extracts
CSV · TSV · Fixed-width · Mainframe · Excel
APIs
REST · SOAP endpoints
Imagine your data in a graph.
From scattered sources to a governed knowledge graph — without an 18-month ETL project.
1Connect any source.
CSV, API, Postgres, Snowflake, Salesforce — Sense ingests them as-is. No schema migration. No pipelines to maintain.
2The graph builds itself.
Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.
The old way
With Sense
- 6–12 months to build ETL pipelinesWeeks to first pipeline
- Manual schema mapping by data engineersAI-driven classification from your SMEs
- Nightly batch refreshesContinuous change data capture
- Duplicates caught downstreamMatched and merged at ingestion
- Warehouse as the golden copyKnowledge graph as the living truth
- Choose: centralize or leave in placeHybrid: golden records + R2RML federation
Key benefits of resolved, governed data.
When every system agrees on who’s who and what’s what, everything downstream gets easier — dashboards, models, and LLMs included.
A single, accurate view of every entity
Customers, suppliers, and products resolve to one governed profile with attribute-level provenance — so every team and tool answers from the same identity.
Less manual cleanup, faster onboarding
Automated classification and steward review replace hand-coded matching rules, collapsing onboarding from multi-quarter ETL projects to weeks.
Data quality you can measure
Completeness, consistency, and timeliness scores attach to each fact as it’s mapped, so downstream consumers see trust signal next to value.
AI and analytics that agree
Models and dashboards inherit whatever ambiguity you feed them. Resolved, disambiguated entities give them one version of the truth to reason over.
Common use cases.
Anywhere two systems disagree about who’s who, resolution pays for itself. These patterns show up in nearly every enterprise.
Customer 360
Resolve CRM, ERP, billing, and support systems into one governed customer profile — the foundation for service, sales, and personalization.
Supplier & product mastering
Consolidate conflicting supplier and product entries across procurement, inventory, and finance into clean, connected master data.
Fraud & risk screening
Link accounts, addresses, and transactions that belong to the same actor, so risk teams see networks instead of isolated rows.
AI-ready data
LLMs and agents are only as grounded as the data beneath them. Disambiguated entities are the prerequisite for enterprise AI that cites its sources.
M&A & systems consolidation
Merge overlapping systems after an acquisition without a rip-and-replace — resolve first, then decide what migrates and what federates.
Regulatory reporting & audit
Report on entities rather than fragments, with lineage from every figure back to its sources when auditors ask.
A different output model than ETL or MDM.
Traditional ETL produces tables. MDM platforms produce mastered tables. Sense produces governed semantic knowledge with hybrid output, continuous sync, and federated virtual graph access.
Resources for evaluating Sense.
Whitepapers, webinars, and case studies to help you understand and evaluate Fluree Sense.
Framework
Model, Map, Connect — how Fluree builds sustainable knowledge systems
The framework behind Sense: ontology modeling, AI classification, and embedded security across every source.
Read the articleThe complete guide to retrieval, knowledge graphs & LLMs.
Why classified, entity-resolved data is the missing layer in enterprise AI.
Download
Webinar replay
Webinar replay
The Future of RAG — Graph-Native AI with Fluree
Why grounded retrieval starts with classified, resolved, governed data.
Watch replayFAQ
No. Master data and decision-critical facts persist in Fluree Core, while high-volume operational detail stays in your source systems and remains queryable through R2RML federation.
Sense continuously captures changes from connected systems and runs them through the same classification and resolution pipeline that built the original dataset — no nightly batches or manual re-imports, so analytics and agents always see the current state.
Sense ships connectors for relational databases like Oracle, PostgreSQL, MySQL, and SQL Server; enterprise applications like SAP, Salesforce, Workday, and ServiceNow; warehouses and lakehouses like Snowflake, Databricks, and BigQuery; plus files, mainframe extracts, and REST or SOAP APIs.
New to entity resolution? Read the complete guide.
Your data has been waiting for this.
Stop building brittle ETL pipelines that break when schemas change. Turn your messiest structured data into a governed, connected, continuously synchronized asset.