Unlocking data intelligence with gen AI-powered SQL queries
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Swiftly uncovering useful and actionable insights from a sea of data is critical for business agility. However, the process of querying databases, analyzing raw data, and generating reports can be daunting for seasoned analysts. In such situations, enterprises need tools that simplify data analysis by converting natural language questions into accurate SQL queries to generate insights in a few seconds. Such solutions democratize data access and insight discovery to empower informed business decision-making.
This blog introduces a solution built by data experts from Sigmoid specifically to solve these challenges. Our solution leverages SQL-to-text intelligence and gen AI-powered query generation to offer users highly accurate insights with greater than 95% reliability. We’ll explore how gen AI converts complex queries into intelligent, context-aware responses that democratize data analysis.
Making data widely accessible with on-demand insights
Traditional data querying often requires writing SQL commands to retrieve specific information from vast datasets. While data scientists or analysts are familiar with SQL and coding, non-technical users can be at a disadvantage. This slows down data exploration and increases the risk of errors. Businesses need an intuitive solution that allows all teams — whether marketing, operations, or finance—to access data without technical dependencies. Our solution is tuned to address multiple use cases across the enterprise, ensuring wider application.
Using gen AI, the SQL-to-text retrieval solution allows users to ask questions in natural language, which are automatically converted into accurate SQL queries. This workflow speeds up response times and eliminates the need for SQL expertise. Powered by advanced NLP, LLMs, and ML algorithms, the solution interprets and responds to queries in real time. The goal is to bridge the gap between data usability and business intelligence, deriving valuable insights from unstructured enterprise data and delivering transformative results.
Gen AI-powered SQL querying solution comes with multiple capabilities
Unlocking greater insights with robust reliability measures
Trust in data provides a solid foundation for data analysis that supports well-informed decision making and improved business outcomes. Our tool prioritizes accuracy and reliability, enabling businesses to trust the insights they receive and make confident choices. To achieve this, our development team follows a rigorous ‘evaluation-first’ approach, embedding reliability throughout the system.
Fine-tuning accuracy with offline evaluation
What sets our solution apart is its extensive offline evaluation process, designed to ensure maximum accuracy in query responses. Before deployment, the tool undergoes a comprehensive evaluation using a proprietary system that simulates real-world scenarios, rigorously testing potential queries in real-time. The dual aim is to fine-tune the tool’s SQL query generation and guarantee consistently accurate results. This intensive testing ensures the tool performs optimally in live environments, minimizing errors and building user trust.
Custom datasets for business-specific problem
A standout feature of our solution is the creation of custom “golden datasets” tailored to each client. This dataset, carefully curated to reflect the client’s unique data, allows the tool to be fine-tuned for their specific business context. As a result, the insights generated are not only accurate but also deeply relevant. Unlike generic tools, this tailored approach ensures peak performance for each business, minimizing errors and ensuring reliable results.
Ensuring quality with pre-deployment checks
Before any query is executed, the tool performs rigorous pre-query validation to confirm data integrity and SQL relevance. This proactive step prevents errors and guarantees accurate, actionable insights. Additionally, robust error-handling capabilities ensure that ambiguities are resolved quickly, with the tool prompting users for clarification when necessary, keeping the process efficient and error-free.
Fig: Relevance and logic evaluation
Transforming data access, analysis, and action across the enterprise
Sigmoid’s data science team plays a crucial role in the development process of this solution that sets new benchmarks for reliability. Our team of experts has leveraged years of experience in database architecture, analytics,machine learning algorithms, and LLM with a deep understanding of data complexities to design a solution that significantly improves decision-making.
The solution executes multiple actions to transform user queries into precise, actionable insights:
Users enter a query in natural language. The system’s NLP capabilities interpret the query and initiate a search for relevant data.
In case of an ambiguous or unclear query, the system employs advanced algorithms to refine and clarify the request, ensuring that the correct data is retrieved.
The system searches across multiple databases, retrieving relevant KPIs and data points, which are then integrated into the query context.
Based on the refined query, the system constructs a detailed prompt and generates a structured SQL command using an LLM fine-tuned for SQL generation.
Based on scores and logical accuracy, the generated SQL command is evaluated for relevance. Errors or logical inconsistencies are resolved to ensure accurate and meaningful results.
The validated SQL query is executed, and the results are presented in a structured format. AI helps to generate a concise overview and summary of the results. Dynamic visualizations allow business users to interact with data.
User feedback on the accuracy and relevance of the results is used to fine-tune the system, ensuring that it continuously adapts to the evolving needs of the business. Continuous monitoring improves performance over time.
Conclusion
In a time when using data strategically can be a game changer, Gen AI-led data analysis represents a significant advancement in how businesses access and utilize information. By making data more accessible and actionable, such tools empower users across the organization to make informed decisions quickly and confidently. With data intelligence being a query away, users can uncover unexpected insights for decision making that drive long-term success and differentiation. We are constantly working on improvements and addition of new features, including predictive analytics for forecasting future trends, generation of full narrative reports, integration with external data sources, and in-platform collaboration for different teams, with the goal to elevate time-to-insights and drive higher productivity.
About Authors
Venkateshwar Reddy Jambula is the Principal Data Scientist at Sigmoid, leading Generative AI solutions for enterprise clients. With over a decade in AI across automotive, mobile, and embedded systems industries, he combines deep research expertise with practical applications. Venkateshwar specializes in optimization systems, NLP, reinforcement learning, and GenAI, contributing to academic publications and developing innovative technologies in anomaly detection and feedback analysis.
Rahul Kushwaha is a Data Scientist at Sigmoid with a growing focus on generative AI, complemented by experience in retail, banking, and beverage industries. An engineering graduate from NIT Jamshedpur, he combines his skills in GenAI, predictive modeling, machine learning, and data visualization to uncover insights and support data-driven decision-making for leading enterprises.
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