XM-AI
Intelligent Database Agent
Easy AI-Powered SQL Chat Agent
Our AI-Powered SQL Automation Tool streamlines SQL query creation for both business analysts and developers. It enables effortless query generation directly from table structures and allows users to craft SQL statements through natural language when documentation is unavailable, ensuring accessibility for all skill levels.
Powered by XM-AI custom logic and an advanced large language model, the platform delivers real-time SQL generation, intelligent optimization, error detection, and context-aware recommendations. Users can also upload existing SQL queries to help the system better understand table relationships and produce more accurate results.
In addition, the tool leverages integrated FAQ data, enabling users to ask contextual questions and receive precise, knowledge-based responses.
By automating complex workflows, improving query accuracy, and enhancing database management efficiency, this AI-driven solution transforms how organizations build, analyse, and optimize SQL queries—leading to faster insights, higher productivity, and smarter decision-making.
“Converting Natural Language Questions into Accurate, Data-Driven Answers”
Business Problem
Manual SQL Query Challenges:
- Significant time investment: Developing SQL queries from the ground up often requires several hours, resulting in delays in reporting and decision-making.
- High skill requirements: Only experienced developers can effectively interpret and work with Organization’s complex data architecture.
- Susceptibility to errors: Even minor inaccuracies can cause query failures, leading to extensive debugging and reduced productivity.
- Dependence on technical experts: Business users and analysts must rely heavily on IT teams for every query, limiting autonomy and agility.
Need for Automation, Accuracy, and Efficiency:
- Accelerated query generation: AI-driven automation enables the creation of SQL queries within seconds.
- Reduced dependency on specialists: Business users can independently generate queries through intuitive, guided tools.
- Improved accuracy and performance: AI minimize human error and ensures optimized, high-quality SQL output consistently.
XM-AI Features
Transforming Natural Language to Actual Results:
Our solution efficiently transforms SQL schema details into fully optimized SQL queries, significantly streamlining data retrieval. By automating this process, it minimizes manual effort, improves accuracy, and accelerates access to insights. This enables teams to concentrate on strategic decision-making while enhancing operational efficiency and strengthening data-driven outcomes across the organization.
Few-shots For Understanding Better Relationships between Schemas:
We employ few-shot learning techniques to enhance the system’s understanding of database schemas, enabling more accurate interpretation and generation of SQL queries. This approach improves contextual awareness, reduces ambiguity, and ensures higher-quality outputs across a wide range of schema structures and use cases.
Maintain the flow of the conversation:
In our product, we leverage conversational history within the LLM to deliver more context-aware and coherent interactions. By retaining and interpreting previous inputs, the system can better understand user intent, maintain continuity, and refine responses based on prior exchanges. This approach enhances overall reliability, reduces repetitive clarification, and creates a smoother, more intuitive user experience that aligns with real-world workflows and decision-making processes.
Multiple Source of Information:
Our system intelligently determines the most appropriate source for each user request, choosing either to retrieve information from the FAQ database or to execute a real-time query. This dynamic decision-making process ensures that responses are both accurate and contextually relevant. By evaluating the nature of the inquiry, the system optimizes performance, reduces unnecessary computation, and delivers timely, precise results that enhance overall user experience and operational efficiency.
Visualize in Graph Format:
Based on the retrieved data, the system automatically generates a visual graph that highlights key patterns and insights. This dynamic visualization enables users to quickly interpret complex information, identify trends, and support data-driven decision-making. By transforming raw query results into intuitive graphical representations, the system enhances clarity, accelerates analysis, and improves the overall effectiveness of information consumption.
How the System Functions
- The system accepts questions in natural language, using either FAQs or organizational data as appropriate.
- It determines the most suitable information source to produce an accurate response.
- When organizational data is required, the system generates the corresponding SQL query.
- It retrieves the requested information from the organization’s database.
- The retrieved data is then forwarded to a base LLM, such as Google Generative AI, for further processing.
- When a visual explanation is needed, the system generates an appropriate graphical representation.
Product visuals
