# Conversational Analytics

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

1. 01 File a ticket with the BI team.
2. 02 Analyst stitches CRM + billing + support.
3. 03 Receive a static deck. It’s already stale.
4. 04 Ask a follow-up. Start over.

60–80% of dashboards built this way go unused.

Fluree

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.

## What Is Conversational Analytics

$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.

Connect any source.

CSV, API, Postgres, Snowflake, Salesforce — Fluree ingests it as-is. No schema migration. No pipelines to maintain.

Search 300+ sources…AVAILABLE SOURCES
Salesforce App · OAuth
SnowflakeData lake
PostgresDatabase · replicate
customers.csvCSV · 1.24M rows
1.24M rows staged
streaming to Fluree · CONNECTED

The graph builds itself.

Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.

Customer
Order
Product
Contract
Owner

Answers, with receipts.

Ask in plain language. Every answer traces back to the exact row it came from — for humans, agents, and apps alike.

NLMCPRESTSPARQL
Top accounts at risk this quarter?
SPARQL · GENERATED
SELECT ?acct ?arr WHERE { ?acct a fin:Account ; fin:risk "high" ; fin:arr ?arr .} ORDER BY DESC(?arr)
ANSWER 3 accounts at elevated risk
Acme Robotics
Northwind Co
Globex Corp
TOTAL EXPOSURE $1.84M ARR exposed
traced · 4 sources

What Makes This Different

## 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

### One agent queries CRM, billing, support, and product data — virtually merged

Select one dataset or four. Configure collaborators and policies. Publish an MCP endpoint. One agent queries every system side-by-side without moving data or staging ETL — because the semantic layer handles the join.

- Multi-dataset federation on day one
- Virtually merged, never physically moved
- Add or remove datasets without rebuilding the agent

### Every answer includes the actual queries — inspectable, reusable

The agent retrieves the model, learns classes and relationships, translates the question into SPARQL, and executes only against governed data. Every answer includes the query logic so data teams can inspect, validate, and promote it.

- Full SPARQL and source citations under every answer
- No black box — everything explains itself
- Queries can be promoted to production BI pipelines

### Each user’s agent sees only the data they’re authorized to

Policies live in the knowledge graph, not in application code. Every query is filtered by the caller’s governed slice — HR can’t see pipeline, sales can’t see comp, and every access is logged.

- Per-user, per-agent policies enforced at query time
- No prompt injection bypasses data-layer governance
- Every query recorded with role + timestamp

### Claude, ChatGPT, Bedrock, or custom agents — standard protocol

Fluree publishes an MCP endpoint that any compatible client can consume. Use the model you already use — Claude, ChatGPT, Bedrock, LangChain, CrewAI, or a custom agent. No vendor lock-in on the LLM side.

- MCP-native — the emerging standard for agent data
- Bring your own model and orchestration stack
- Human chat, scheduled alerts, or M2M workflows

### LLM-generated dashboards become production BI after review

For complex prompts, Fluree generates React-based dashboards, charts, and summaries tuned to the question. Verified queries can be promoted to BigQuery, Databricks, or any BI stack — turning weeks of dashboard work into hours.

- Auto-generated React dashboards from complex prompts
- Promoted SPARQL becomes production BI logic
- Machine-to-machine MCP = scheduled reports + alerts

Questions Your Data Team Gets Asked Every Week

- “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%.”

| Capability | Traditional<br>BI & chat-with-data | Fluree<br>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 |

In Production

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 of 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.”

## FAQ

### What is conversational analytics?

### What is GenBI?

### How is this different from Tableau or Power BI?

### How does Fluree avoid hallucinations?

### Do we need data scientists or a BI team?

### How long does it take to get started?

### Can we use this with our existing BI tools?