Case Study
Private Text-to-SQL AI Agent for Manufacturing Analytics in Power BI
Solution
AI & ML, Automation/Agents, Data Engineering
& Analytics, BI & Reporting
Industry
Healthcare
Core Technology
AWS, Python, PowerBI
Overview
In the high-stakes, complex environments of global manufacturing, data is the lifeblood of operational excellence. However, for our client, a prominent industry leader in medtech, this data remained locked behind a formidable barrier of technical complexity.
With a production schema exceeding 1,100 tables and 60–70 columns per table, the client’s engineers were tethered to a small team of data analysts to translate business questions into SQL queries. This dependency created a chronic bottleneck, turning simple data requests into multi-day endurance tests and risking the loss of institutional knowledge as personnel transitioned.
Xavor was brought in to eliminate the data analyst bottleneck through AI solutions by enabling engineers and management to query complex databases using natural language, without writing SQL.
Business Challenge
The client’s analytics workflow had become a “gatekeeper” model that stifled agility. The primary constraints included:
- Data Analyst Bottleneck: Engineers depended on a limited number of data analysts to write SQL queries, creating significant workflow delays
- Database Complexity: Massive database schema (1,100+ tables, 60-70 columns per table) that few people fully understood
- Institutional Knowledge Loss: As data analysts joined and left the organization, critical knowledge of the complex database schema was being lost
- Workflow Inefficiency: Multi-day turnaround times for simple data requests due to request queuing and clarification cycles
- Scaling Limitations: Single data analyst handling requests from multiple engineers simultaneously
the solution
Xavor engineered a secure, domain-aware, text-to-SQL agentic system to democratize data access. The solution combined domain-aware retrieval, enterprise AI services and private tooling, and an agentic learning loop.
Domain-Specific
Intelligence
To ensure surgical accuracy, Xavor organized knowledge around key operational areas: Scrap, Rework, PVA, and FPY/Yield. A simple dropdown interface routes questions to the most relevant knowledge base, improving both speed and correctness by narrowing the search space.
Text-to-SQL Generation Grounded in Expertise
The system learned from historical analyst queries and supply-chain terminology. Users ask questions in plain English (e.g., “What was our scrap rate for product X last quarter?”), and the AI autonomously generates an optimized SQL query and executes it against the production database.
Automatic, Context-Aware Visualization
Utilizing Plotly, the system doesn’t just return rows of data; it intelligently selects the best visual representation—charts or graphs—to make insights immediately interpretable and actionable.
Power BI Integration via Custom Visual
To meet leaders where they work, Xavor built a custom Power BI visual using a React frontend. This allowed executives to pose natural language questions directly within their familiar dashboards, making the AI’s power a native part of their daily analytics routine.
Evolution into a Self-Learning Agentic System
Using the Vanna 2.0 framework, the system captures institutional knowledge at the point of use. When an analyst validates or corrects a query, the system “learns” the logic, ensuring that database expertise is preserved within the AI rather than siloed in individual minds.
outcomes & benefits
The transformation moved the organization from a fragile, human-dependent data gateway to a resilient, democratized system:
95% reduction in query response time
plummeting from 120–150 seconds to a mere 5–7 seconds.
Knowledge Preservation
Created a centralized, searchable repository of supply-chain and schema expertise.
Executive Accessibility
Power BI integration enabled leadership to query data conversationally, supporting faster insight access in existing workflows.
Significant reduction in routine
SQL requests
enabling data analysts to focus on higher-value work.
Risk Mitigation
A private alternative eliminated exposure of sensitive data to public consumer AI platformsby enabling natural-language querying within governed enterprise systems.
Tools & tech stack
conclusion
This engagement exemplifies modernizing not just a tool, but an organizational capability. By bridging the gap between complex database architecture and intuitive natural language, Xavor helped the client reclaim agility. The solution demonstrated that the most effective AI systems are those that amplify human expertise while learning and adapting through real usage.
Is your organization’s data trapped behind technical complexity and resource bottlenecks? Let Xavor Corporation help you build a smarter, more connected enterprise. Visit www.xavor.com to learn how our AI and data engineering expertise can transform your operational analytics, or contact us today for a personalized consultation.
Don’t wait days for simple data answers
If you’re struggling with a complex data ecosystem, we can make your data accessible, intelligent, and self-learning.