Among our currently developed internal projects is DataLoam – a modern SQL client with integrated AI functions, which we design as a tool for more efficient work with databases and data analysis.
Why is DataLoam being created?
Working with databases often involves writing complex SQL queries, navigating data structures, and optimizing performance. DataLoam aims to make this process more accessible, faster, and simpler through artificial intelligence assistance.
Users can enter a query in natural language – for example: "How many orders came in the last 30 days?" – and DataLoam will generate the corresponding SQL query that can be immediately executed or further modified.
The Challenge
The project's goal is to develop a tool that speeds up daily database work using AI, while remaining fully under user control. DataLoam should be a practical tool for developers and analysts – without compromises in security or performance.
DataLoam will support various database systems (PostgreSQL, MySQL, SQLite...) and offer features such as:
- AI generation of SQL queries based on natural language
- Advanced database indexing based on schema, data, and past queries
- Formatting and clear query editing directly in the editor
- Customizable user interface according to user preferences
- Automatic SQL completion considering database structure
- Data export to formats like CSV, JSON, or Excel
- Result visualization in the form of interactive tables and charts
User privacy is crucial for us. The application may in some cases need a small sample of real data to understand the database structure – but this happens exclusively with user consent and data is stored only locally. AI runs in the cloud, but all queries, history, and other content is normally stored only on the user's device.
Why isn't ChatGPT enough?
Regular use of tools like ChatGPT for generating SQL queries has its limits – it doesn't have access to the specific database, doesn't know the current schema, and users must manually describe the context and data they're working with. This is not only time-consuming but also prone to errors.
DataLoam has a crucial advantage: because it runs as a full-featured SQL client, it has constant access to the database structure – it knows what tables and columns exist, and how they're connected. There's no need to repeatedly enter context – the AI sees it automatically.
Moreover, thanks to locally available data (stored with user consent), we try to actively improve generated queries according to actual values in the database – for example, recommend more suitable filters, or suggest more efficient queries.
Because it's a full-featured client, all the usual features like query execution, history, exports, or visualization are included. Moreover, every AI-generated SQL is first verified locally – if it's not valid, we automatically return it to AI for correction. In regular chat with GPT, this entire process would have to be done manually.
Our Solution
We're building the project on a modern technology stack – Electron + React for a multiplatform desktop application, .NET for backend and custom AI model integration for natural language processing and SQL generation.
- Modular architecture for easy extension to additional database types
- Desktop application running on Electron with React frontend and .NET backend for database logic management and secure AI communication.
- Automated version distribution with support for updates and test builds for various platforms (Windows, macOS, Linux)
What the project brings us
Through DataLoam development, we're gaining deeper experience in areas such as working with LLM (large language models), designing secure interfaces for data work, and building tools for developers and analysts. The project also helps us improve internal dev processes and CI/CD pipeline for desktop applications.
Not just software – we understand the product too
DataLoam isn't just a technical project – it's a product we're building from idea to market launch. Besides the actual development, we also focus on product management, marketing, and sales.
This way we know what's needed for successful tool deployment in real operation – from verifying user needs through pricing to scalable distribution. We don't just focus on code, but on the entire product as a whole.
This experience is reflected in our collaboration with clients – we understand business logic, know how to ask the right questions, and look for practical solutionsthat make sense not only technically but also commercially.
Development previews
The application is still in development, but we already have a functional prototype with AI assistance and visual query editor.
Summary
DataLoam is a project that combines AI, UX design, and developer productivity in one tool. Even though it's still in development, we already see its potential to simplify daily data work for both developers and analysts. We believe its completion will bring significant efficiency improvements to internal and client projects.