Ask the question. Get the cited answer. Skip the ticket.
Fluree connects your CRM, billing, product, and support systems in a governed knowledge graph. Ask anything in plain English — every answer comes back with verified SPARQL and full source citations. No analyst queue. No stale dashboards.
Before
T + 3–5 days- 01File a ticket with the BI team.
- 02Analyst stitches CRM + billing + support.
- 03Receive a static deck. It’s already stale.
- 04Ask a follow-up. Start over.
60–80% of dashboards built this way go unused.
After
T + 3 seconds↓ asked
“Which customers are about to churn?”
- Live dashboard across 4 systems.
- Verified SPARQL under every metric.
- Every number traces to a row.
Delivered via MCP. Works with Claude, ChatGPT, Bedrock, and any compatible client.
$30B on BI software. Most of it goes unused.
Traditional BI fails because it can only answer dashboards someone thought to build in advance. Forrester finds analysts lose 12 hours a week searching siloed data; 60–80% of dashboards go unused. Plugging an LLM directly into databases produces confident answers that are frequently wrong — the model doesn’t understand how your data relates.
Conversational analytics is different. A user asks a question in plain English. An agent retrieves the semantic model, learns the classes and relationships, translates the question into verified SPARQL, and executes against governed data. Every answer comes back with the query logic attached.
Fluree is the only platform delivering the full stack natively — which is why we reach 95%+ accuracy where “chat with your data” tools hit an 80% ceiling.
Three steps.
One platform.
From a raw source to a governed knowledge graph — with answers that trace back to the row they came from.
Connect any source.CSV, API, Postgres, Snowflake, Salesforce — Fluree ingests it as-is. No schema migration. No pipelines to maintain.
The graph builds itself.Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.
Answers, with receipts.Ask in plain language. Every answer traces back to the exact row it came from — for humans, agents, and apps alike.
Six architectural bets that separate us
from “chat with your data.”
The LLM reads what a field means before it writes a query
In Fluree, the model is data itself — queryable, introspectable, and richly described with labels, comments, types, and explicit relationships. That means the LLM can understand your business vocabulary without prompt engineering.
- RDFS labels teach the LLM what every field means
- Zero-shot accuracy without prompt tuning
- The graph becomes a living business dictionary
They used to disappear into a backlog. Now they get answered — with the SPARQL attached.
“Which of our top 20 customers are at risk of churning next quarter?”
“What are the five worst-performing products, and why?”
“Produce an executive dashboard showing ROI growth and current risks.”
“I’m new to the organization — help me understand our data.”
“What camera-trap evidence do we have for nocturnal species, 2018–2022?”
“Total CRE exposure over $500K in downtown markets with LTV above 75%.”
“Which of our top 20 customers are at risk of churning next quarter?”
“What are the five worst-performing products, and why?”
“Produce an executive dashboard showing ROI growth and current risks.”
“I’m new to the organization — help me understand our data.”
“What camera-trap evidence do we have for nocturnal species, 2018–2022?”
“Total CRE exposure over $500K in downtown markets with LTV above 75%.”
Traditional BI & chat-with-data vs.
Fluree Conversational Analytics.
See how Fluree stacks up against traditional BI tools and chat-with-data solutions across the capabilities that matter most.
Capability | Traditional BI & chat-with-data | Fluree Conversational Analytics |
|---|---|---|
Query method | Pre-built dashboards, or vector-based chat-with-data | Plain English + verifiable SPARQL on a knowledge graph |
Cross-system queries | Requires ETL, or single-warehouse only | Native multi-dataset federation |
Accuracy | Analyst-dependent, or ~80% ceiling | 95%+ with GraphRAG |
Answer provability | Limited, or black-box | Full SPARQL + source citations under every answer |
Time to first answer | Days to weeks | Seconds |
Dashboard creation | Manual by analysts, or AI on schema | AI-generated from the semantic model |
Unstructured data | Not supported or limited | Docs, audio, video — extracted and governed in the graph |
Security model | Application-level | Data-centric, per-user policies in the graph |
Standards | Proprietary | W3C (RDF / SPARQL) + MCP |
Global financial services leader
“Semantic tagging went from error-prone and manual to quality-controlled and AI-driven. User trust in the data portal came back.”
~500K
documents
100%
automated tagging
Hundreds
analysts served daily
10×
knowledge base growth
“In the age of AI and agents, everybody deserves AI that works. Let’s make sure we get it done.”
Read the full case studyThe conversational analytics playbook.
Webinars, whitepapers, and articles that walk through the architecture, the evidence, and the production path.
Unified Intelligence — Ask the Questions That Matter, Across Every System That Matters
A live MCP agent conversation spanning Salesforce, SAP, spreadsheets, and SQL — at 90% less cost than traditional integration.
Watch replay
Introducing Business Intelligence Without the BI Software
The shift from pre-built dashboards to conversational, governed answers — and what it means for BI teams.
Read the articleThe complete guide to retrieval, knowledge graphs & LLMs.
Download whitepaper
The Power Trio Reshaping BI: GraphRAG, MCP & LLMs
Why these three together produce agents that actually work in production — end-to-end architecture.
Read the articleRecognized by Gartner
Stop building dashboards. Start getting answers.
60–80% of dashboards go unused. Fluree gives every person in your organization the answers they need — instantly, verifiably, from actual data.