Why do traditional reports often fall short of business goals?
Traditional reporting shows what has already happened and relies on pre-defined, repetitive questions. However, modern businesses need much more than quick access to raw numbers.
Conversing with data through AI allows you to break free from rigid frameworks: it helps users explore why something happened, what it might mean, what to investigate next, and which business hypotheses are worth testing. This is a complete paradigm shift – a seamless transition from “show me another report” to “help me understand the data and decide what decisions to make next”, allowing organizations to rapidly and interactively explore new, urgent and yet-to-be-defined market challenges.
When does a dedicated Chat with DB win over off-the-shelf solutions like Fabric Data Agent?
Universal, mass-market tools have their technological limitations, quickly becoming insufficient for demanding managers. Our proprietary Chat with DB was designed for organizations seeking a solution tailored precisely to their unique business needs. This dedicated system avoids the limitations of off-the-shelf platforms and is not associated with the rigid licensing costs of Microsoft Fabric. Instead, it provides the company with complete flexibility, complete ownership of the technology and complete control over how data is analyzed.
Why does our AI assistant act like an experienced business analyst, not a standard chatbot?
Unlike simple chatbots, which typically translate text into database queries, our system has an advanced logical layer (agent orchestration). It operates like advanced mental models: it first analyzes the intent of the query, decides what data is needed and then can execute a series of several different queries in the background. Crucially, before displaying the answer, it thoroughly checks and verifies whether the result makes business sense. It also has self-correcting mechanisms – if an AI-generated query triggers a database error (e.g., due to a table name error), the assistant immediately corrects its own SQL code and automatically retries, instead of helplessly displaying a failure message to the user.
How to safely develop analytics in a company without the risk of incorrect answers from AI?
The solution is designed to evolve in a fully controlled manner. Instead of relying solely on artificial intelligence interpretation, the system leverages a constantly growing library of pre-validated business questions and SQL queries.
When a user asks a question, the system first looks for similar, pre-validated questions that have already been tested and paired with valid SQL code. It can then use these examples as a guide before generating or executing the query. This reduces the risk of incorrect answers, eliminates AI hallucinations, and creates a practical mechanism for continuous improvement. Each new, company-approved question expands the system’s future analytical scope. This makes the solution significantly more reliable than generic text-to-SQL tools and provides organizations with a structured way to safely develop their analytical expertise.
What makes an assistant not only pull out raw numbers but also understand the unique context of your company?
Chat with DB solution can be easily enriched with internal organizational knowledge, such as SharePoint documentation, metric definitions, business rules, dictionaries, corporate policies, and historical analysis. This adds business context to the data layer and helps the system support not only responses but also interpretation and guided analysis.
CASE STUDY: How can decision-makers extract key data from a company in seconds and make business decisions based on it?
✋ Important note at the outset: The data used in the following example is completely fictitious and has nothing to do with the actual performance of the brands mentioned (no worries, Burger King!). The names of well-known restaurant chains have been used in this article solely as an example to illustrate how our assistant can handle data analysis in large, multi-location organizations.
In a multi-location business – as seen in major chains like Burger King, Popeyes Louisiana Kitchen, KFC, VICIO, McDonald’s, Starbucks or Subway – the key to success lies in extracting immediate, actionable insights. Instead of waiting days for complex reports to be prepared, CFOs, COOs, and operations managers can make critical decisions in a matter of seconds, analyzing sales, marketing, and financial data for every single location through a simple chat interface.
🎬 Here is what this proces looks like in a conversation with our data assistant:
Custom prompt: The user enters a simple, everyday question: “What is the average revenue per transaction by order channel and menu category?”
The AI agent analyzes this question in seconds, converts it into a database query (SQL), and queries the database, giving you a ready-made answer to your question. You don’t need to know any code or technical structures – the system does it all for you.
Transforming data into clear insights and ready-made decisions: Within seconds, the system generates clean tabular summaries. However, the greatest value appears right below them – in the “Key Insights” section. The AI automatically translates numbers into human language, pointing out the most critical phenomena, such as catering orders dominating in terms of transaction value.
Automatic self-correction in the background: In this video, we intentionally show a challenging scenario in which the assistant creates SQL code for a completely new, custom analysis. The system acts like a real analyst: when a query sent to the database encounters an inconsistency (e.g., a hidden change in a table name), the assistant doesn’t freeze. It instantly reads the system’s feedback (visible in red in the log), logically interprets the error, automatically corrects its code, and retries until it succeeds.
🎬 Check out our second video where we show how the assistant handles subsequent questions:
The recommended architecture is a single, shared data conversation engine responsible for understanding questions, retrieving reference queries, generating SQL code, executing it, formatting responses, interpreting results, logging, and enforcing security controls. This exact same engine can be made available to employees across a wide variety of formats:
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- A web application for business users,
- Microsoft Teams or similar corporate communication channels,
- An MCP (Model Context Protocol) server for advanced users working directly with AI tools.
- Future integrations with other AI-enabled workflows.
Why modern data conversation does not replace Power BI?
Strategically speaking, the implementation of our solution is in no way intended to replace Power BI, certified dashboards or managed reporting. These tools remain absolutely essential for official and repeatable reporting within the organization.
The real opportunity lies in complementing them with an artificial intelligence layer that reduces existing reporting backlogs, accelerates exploratory analysis, and helps business teams seamlessly transition from mere data access to deep insights, interpretation and significantly better decision-making.
How can you ensure the highest standard of security and access control?
From a security and compliance perspective, the solution can be aligned with enterprise standards by leveraging Azure AI Foundry, Microsoft Entra ID, role-based access and controlled data execution. In future production stages, row-level security (RLS), effective identification, on-behalf-of access patterns, and precise authorization policies can help ensure that users only see data they are already authorized to access.
How to take the first step and see the system in action?
We begin the tool’s implementation with a secure, controlled pilot project (Proof of Concept). While the pilot is not yet a finalized production platform, it allows the company to verify whether this rapid, conversational access to knowledge delivers real value to the organization. There are three testing paths to choose from:
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- Web App PoC – for business users who need a simple interface to ask questions and receive interpreted answers.
- MCP PoC – for power users who want to access the data conversation engine directly from AI tools.
- Combined PoC: Web App + MCP – to validate both user groups and both access models in parallel.
Do you have higher expectations for your data analysis? Let’s see how we can meet them! Send us a private message or leave a comment under this article. We’ll be happy to arrange a brief, no-obligation demo meeting for you, where you can test the assistant with your own business questions and see it respond to them in real time!
Contact: bartosz.rutkowski@datariselab.com
Book a meeting: here