See Sense on your data.
Walk through your sources with a Fluree solutions architect.
Turn siloed data into connected knowledge
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 downstreamEntity resolution at ingestion
- Warehouse as the golden copyKnowledge graph as the living truth
- Choose: centralize or leave in placeHybrid: golden records + R2RML federation
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.
| Capability | Traditional ETL | MDM | Fluree Sense |
|---|---|---|---|
| Discovery & Onboarding | Manual config | Manual config | AI-driven classification |
| Schema Mapping | Hand-coded transforms | Rule-based | ML-trained from SMEs |
| Entity Resolution | Separate tool needed | Built-in | Built-in, continuous, with governance APIs |
| Output Format | Tables / flat files | Tables | Semantic knowledge graph (JSON-LD + R2RML) |
| Continuous Sync (CDC) | Requires extra tooling | Limited | Native, bi-directional |
| Data Lineage | Varies by tool | Varies | End-to-end, built into graph |
| Knowledge Graph Ready | No | No | Direct load to Fluree Core |
| Human-in-the-Loop | Not typical | Some support | Built-in adjudication queues |
| Federated Query Output | No | No | R2RML virtual graph views |
[ Sense in the Fluree platform ]
Sense is the structured data on-ramp.
Sense brings structured systems — databases, SaaS, files, APIs — into the same governed semantic model that CAM, Core, and ITM share.
- Fluree ITM
defines the language.
- Sense
maps structured data into that model.
- CAM
extracts knowledge from unstructured content against the same vocabulary.
- Core
persists the resulting graph as governed enterprise truth.
- Fluree AI
turns that connected knowledge into grounded assistants, retrieval experiences, and agent workflows.
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 replayYour 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.