Accessing complex business data shouldn’t require deep technical expertise. However, one of our clients, a software company specializing in analytics for business owners, faced significant challenges when it came to empowering their users to access insights efficiently. Their customers, primarily business owners managing multiple locations, struggled to retrieve key analytics due to the complexity of writing SQL queries. To address this, we implemented a powerful text to SQL solution, enabling seamless access to data and transforming their product into a user-friendly, value-driven tool.
Story about SaaS platform that streamlines in-store operations for retailers by providing mobile-first solutions for audits, task management, and analytics. Founded in 2018, it enables businesses to enhance efficiency, ensure compliance, and improve customer satisfaction through real-time data insights and workforce optimization.
For businesses managing multiple locations, timely access to analytics such as top-performing stores or revenue trends is critical. However, our client’s existing analytics platform relied on SQL-based queries to retrieve data, which presented several challenges:
High technical barrier: Writing SQL queries required advanced programming skills, making the platform inaccessible to non-technical users.
Time-consuming processes: Even with technical teams, creating and validating queries consumed significant time, delaying decision-making.
Limited user satisfaction: The complexity of the platform discouraged usage, reducing customer satisfaction and product adoption.
These challenges created an urgent need for a solution that would simplify the process of data retrieval while enhancing the platform’s usability.
Recognizing the need to make data more accessible, we integrated a cutting-edge text to SQL solution into the client’s analytics platform. This backend-focused solution eliminated the need for manual SQL query writing, enabling users to interact with the system through natural language queries.
Leveraging large language models (LLMs)
Our text to SQL solution was built on advanced on-premise large language models (LLMs), capable of accurately converting natural language inputs into SQL queries. This ensured that users could ask questions like “What’s the most profitable store this month?” and receive immediate answers.
Backend integration without frontend dependencies
To maintain simplicity and efficiency, the solution was implemented as a backend feature, seamlessly connecting with the client’s existing relational database. By focusing on backend integration, we avoided the need for frontend reconfiguration, ensuring a streamlined deployment process.
Real-time query processing and data access
The text to SQL system processed user queries in real-time, delivering analytics instantly. This significantly reduced delays and allowed business owners to make quick, informed decisions.
Accuracy and continuous improvement
At launch, the model achieved an accuracy of 65% in converting natural language queries into correct SQL outputs. While this level of precision delivered immediate value, we also committed to refining the model to reach 80% accuracy through ongoing R&D and user feedback.
Text-to-SQL technology bridges the gap between natural language inputs and database queries, enabling users to interact with relational databases without requiring SQL expertise. This technology leverages advanced large language models (LLMs) trained on diverse datasets to interpret user queries in natural language and accurately convert them into structured SQL statements. The process involves multiple stages: natural language understanding (NLU) to parse user intent, schema mapping to align queries with the database structure, and SQL generation to produce executable statements. Text-to-SQL systems are further enhanced through feedback loops and domain-specific fine-tuning, ensuring high accuracy and relevance to specific use cases. By automating query formulation, text-to-SQL eliminates technical barriers, allowing non-technical users to retrieve insights efficiently. This innovation is particularly valuable for businesses, enabling real-time analytics, faster decision-making, and broader adoption of data-driven practices.
The text-to-SQL technology by Shperling stands out due to its advanced capabilities, seamless integration, and focus on delivering tailored solutions for complex business needs. Key advantages include:
High Accuracy Through Fine-Tuning
Shperling’s text-to-SQL leverages proprietary large language models (LLMs) and domain-specific fine-tuning, achieving superior accuracy in converting natural language queries into SQL statements. This ensures precise alignment with client database schemas and reduces the risk of errors.
Scalability and Performance
Designed to handle large-scale datasets and high query volumes, Shperling’s solution supports businesses with extensive operations without compromising performance or response times.
Customization for Specific Industries
The system is adaptable to industry-specific needs, enabling optimized queries tailored to the unique requirements of sectors like travel, retail, and consulting.
Seamless Integration
Shperling’s text-to-SQL integrates smoothly into existing database infrastructures, eliminating the need for costly overhauls or additional middleware, while ensuring compatibility with relational databases.
Enhanced User Accessibility
By removing the need for technical expertise, the technology empowers non-technical users to access complex data, democratizing data-driven decision-making across all levels of an organization.
Continuous Improvement
Shperling’s R&D focus ensures ongoing enhancements, aiming for a high standard of 80%+ accuracy, backed by real-world testing and client feedback.
With Shperling’s text-to-SQL technology, businesses gain a competitive edge by simplifying data retrieval processes, accelerating decision-making, and unlocking the full potential of their data.
The journey to implement the text to SQL solution was both collaborative and methodical. Here’s how we approached it:
Understanding user needs
We conducted in-depth consultations with the client’s team to understand their users’ most common queries and pain points. This informed the design of a solution that was not only functional but also intuitive.
Training the LLM
The large language model was trained on real-world data from the client’s platform, ensuring it could handle industry-specific terminology and unique query structures. This customization was critical to achieving high initial accuracy.
Testing and refinement
The text to SQL solution underwent rigorous testing to ensure reliability and user satisfaction. Feedback from early adopters helped identify areas for improvement, which were addressed before the full-scale rollout.
Phased deployment
To minimize disruption, the solution was deployed in phases, starting with a beta version for select users. This approach allowed the client to gather valuable insights and make adjustments before introducing the feature to their entire customer base.
The impact of implementing the text to SQL solution was profound, delivering measurable benefits for both the client and their end-users.
Lowering the barrier to analytics
By eliminating the need for SQL expertise, the platform became accessible to all users, including those with no technical background. Business owners could now retrieve complex insights with simple natural language queries, making analytics a part of their daily operations.
Faster decision-making
The real-time processing capabilities of the text to SQL system reduced query response times by over 50%, allowing users to act on insights immediately. This efficiency empowered business owners to identify opportunities and address challenges faster than ever before.
Enhancing user satisfaction and engagement
The simplicity and accuracy of the text to SQL feature significantly improved the user experience. Customer feedback highlighted the newfound ease of accessing data, leading to higher engagement and satisfaction rates.
Scalable potential for future growth
The successful implementation of text to SQL not only addressed immediate challenges but also positioned the platform for long-term scalability. The client now had a competitive edge, with a feature that differentiated their product in the market.
One of the most rewarding aspects of the project was hearing directly from users. A business owner shared their experience:
"With the new text to SQL feature, I don’t need to rely on technical staff to get the insights I need. I can just type my question and get the data instantly. It’s a game-changer for my business."
This feedback underscored the value of the solution and its impact on simplifying complex processes.
The text to SQL solution we implemented demonstrates how AI can bridge the gap between technical complexity and user-friendly functionality. Here’s why this technology is essential for businesses:
Removing technical barriers: Text to SQL enables users to focus on insights rather than the mechanics of data retrieval.
Boosting productivity: By automating query generation, teams save time and resources, allowing them to concentrate on strategic tasks.
Enhancing user adoption: A simplified interface encourages more users to engage with analytics tools, driving higher adoption rates and delivering more value.
The success of this project highlights the transformative potential of text to SQL technology. By making analytics accessible, intuitive, and efficient, we helped our client elevate their product and deliver greater value to their customers.
If your business is ready to revolutionize data access and analytics, contact us today to explore how text to SQL can empower your team and drive results: