For all the hype around AI, MCP (Model Context Protocol) actually seems to be delivering on the promise of extracting insights from data. MCP establishes a universal protocol that allows AI models to connect with virtually any external system, tool, or data. 

Whether you ask your LLM to find out how inventory and tariffs are affecting holiday sales, or to analyze financial risk by customer, the MCP acts as the missing infrastructure layer that connects AI directly to enterprise data sources. Instead of returning convincing inaccuracies due to lack of available knowledge, your LLM can suddenly find and contextualize the right answers. It can even provide charts and graphs displaying insights and recommendations. 

If this sounds an awful lot like business intelligence (BI), well … it is. The power trio of an MCP, GraphRAG, and an LLM, which we’ll explain in depth in this article, supports complex questions and generates not just answers, but analytical tools created specifically for your query and data context. Add to that Fluree’s ability to trust and verify your results, and you have something like a just-in-time, enterprise-grade BI system that a company of any size can use. 

Business intelligence is was hard

Extracting actionable insights from enterprise data is hard. Whether it’s understanding true customer 360 metrics beyond simple CRM data, analyzing supply chain impacts from inventory levels to tariff effects, or determining which marketing promotions deliver the best ROI, the process can take days to months. 

Midmarket companies in particular feel the pain. The traditional business intelligence stack requires massive upfront investment, constant maintenance, and a team of skilled SQL developers to keep dashboards updated. Questions like “What are the 5 worst performing products in my dataset compared to competitors?” or “How might holiday sales volumes be impacted by current inventory levels and tariff changes?” demand complex analysis across multiple data sources.

By contrast, midmarket IT teams are already stretched thin maintaining existing systems. Say a CFO needs new analytics to understand payment risk by customer. Too bogged down to create a visually appealing dashboard, IT offers a raw Excel export. The CFO then spends hours trying to get ChatGPT to help with complex Excel formulas, with mixed results. 

RAG to the rescue? 

Retrieval-augmented generation (RAG) models solved some, but not all, of the problems enterprises face with LLMs. RAG models find up-to-date information in databases, run queries, and pull data from authoritative sources, reducing the chance of inaccuracies and hallucinations. Yet the systems still fail at traversing different data sources and finding accurate context. The moment you need to understand relationships across different entities, SQL RAG breaks down. 

This is because traditional RAG is still trapped in application siloes. It is only useful for straightforward queries and single-system analysis. Adding a graph database to RAG, known as GraphRAG, solves the problem by representing data relationships as graphs rather than traditional tables. 

GraphRAG enables LLMs to understand the connections between different data points, enabling more sophisticated analysis. More on the power of GraphRAG here. 

GraphRAG + MCP = Just-in-time BI

When you add MCP to GraphRAG, you find yourself with something resembling just-in-time business intelligence. Through an MCP connection, the LLM can combine internal and external data to autonomously perform sophisticated multi-step analysis. The system navigates database joins intelligently, writes syntactically valid queries, generates custom React.js code to create tailored visualizations, and produces interactive, dashboard-style analytics specific to your question and data.

Let’s return to our CFO example. The CFO wanted to understand payment risk by the customer. She now feeds her raw Excel export to an LLM bolstered by GraphRAG and an MCP. The LLM compares customer data to industry-standard FICO risk brackets, and creates a sophisticated visualization showing risk categories, customer counts, and outstanding balances. The CFO discovers a worryingly large proportion of high-risk customers, and investigates how to remedy the problem. Instead of spending hours grappling for answers, she finds useful insights and begins to solve a real business problem. 

The power trio of GraphRAG, MCP, and LLM cracks open use cases that were formerly out of reach for mid market companies, and took days in larger enterprises. For example:

• Supply chain optimization: analyze how inventory levels, competitive positioning, and external factors like tariffs might impact holiday sales volumes, providing strategic recommendations based on real-time data analysis.
• Customer intelligence: move beyond basic CRM 360 views to achieve true customer insights encompassing sales performance, process efficiency, cost analysis, satisfaction metrics, and renewal patterns.
• Marketing ROI: determine which promotional campaigns deliver the best return on investment through sophisticated cross-channel analysis.

What about security and governance? 

It goes without saying, all these capabilities are moot when there is no data security or governance. Rather than implementing complex API layers for authentication and query modification, Fluree creates virtual databases for each user that contains only the data they’re authorized to access.

For example, HR personnel can access employee data but not inventory information. In wealth management, certain roles can see portfolio data but not personal identifiable information or net worth details. Marketing teams can access campaign data but not financial metrics. This approach ensures that that the LLM can operate freely within each user’s authorized data scope without requiring additional security middleware or query filtering.

A Fluree-powered LLM also has the ability to describe its entire analytical process and link to the databases it queries. Users can retrace every step the LLM took and independently verify results by querying the dataset themselves. We’re pretty confident that with Fluree, your LLM won’t hallucinate, but you now have the tools to check up on every step. 

BI for everyperson

LLMs, MCP, and GraphRAG, plugged into Fluree, are enabling everyone from the midmarket on down to leapfrog BI investments entirely.