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LuminaBI: Generative Business Intelligence & Text-to-SQL

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Brief and project description

Transforming static dashboards into a conversational data analyst that answers executive questions with instant, accurate SQL queries.

LuminaBI transforms the way executives interact with enterprise data. Moving beyond static, pre-built dashboards, this "Generative BI" agent connects directly to your cloud data warehouse (Snowflake/Databricks/BigQuery). It allows non-technical stakeholders to ask complex questions in plain English and receives accurate, data-backed answers, charts, and root-cause analysis in seconds. It effectively puts a Senior Data Analyst in the pocket of every executive.

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The Challenge

A leading Retail Chain sat on terabytes of valuable customer data, yet their decision-making was slow and intuition-based.

  • The “Dashboard Trap”: The Data Team maintained 500+ PowerBI dashboards, yet executives still couldn’t find simple answers like “Why did margin drop in the Northeast region last week?” The dashboards showed what happened, but not why.

  • The Analyst Bottleneck: Every new question required a data analyst to write custom SQL queries. The backlog for new report requests was 3 weeks long, meaning decision-makers were always acting on stale data.

  • Low Adoption: Expensive BI tools were underutilized because the interfaces were too complex for non-technical managers to navigate.

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The Solution

The AI Division deployed built a Text-to-SQL Agentic Interface that democratizes data access without compromising accuracy.


The Workflow Breakdown

1. Deterministic SQL Generation:
Unlike a standard chatbot that might “hallucinate” a number, LuminaBI is engineered to write syntactically correct SQL queries. It queries the actual database to retrieve raw numbers. If the user asks for “Sales in Q3,” it executes SELECT SUM(sales) FROM transactions WHERE quarter = ‘Q3’, ensuring 100% mathematical accuracy.

2. Self-Correction Loop:
The agent features an autonomous debugging layer. If it writes a query that fails (e.g., a column name is wrong), it reads the database error message, corrects its own syntax, and retries automatically—all before the user even notices a delay.

3. Dynamic Visualization:
The agent doesn’t just return a table of numbers. It analyzes the data shape and selects the best visualization—rendering a Bar Chart for comparisons or a Line Graph for trends—instantly within the chat interface.


Technology Stack

  • Agent Framework: LangChain SQL Agent

  • Data Warehouse: Snowflake / Google BigQuery

  • LLM: GPT-4o (Fine-tuned on the client’s specific database schema and data dictionary)

  • Frontend: Streamlit / React (Custom UI)

  • Visualization Library: Plotly (Interactive charts)

The Results

  • 90% Reduction in Ad-Hoc Requests: Executives could answer their own questions instantly, freeing up data analysts to focus on high-value data engineering and modeling.

  • Instant Time-to-Insight: Questions that used to take 3 days (ticket submission to report delivery) are now answered in 15 seconds.

  • High Trust & Adoption: Because the agent displays the SQL query logic (for technical users) and the source data, the organization rapidly trusted the system, leading to widespread adoption across C-Suite and Regional Managers.

Reduction in Ad-Hoc Requests
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Reduction in Time-to-Insight
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