Getting answers and information from a database should (and can) be as simple as having a conversation. So, we built AutoQL to help people access and interact with their data in a natural, intuitive way.
Powered by a suite of proprietary Conversational AI technologies, AutoQL is Chata.ai’s natural language (NL) to database query language (DBQL) translation solution. Through our API, AutoQL receives, understands, and interprets natural language queries input by a user, dynamically generates a corresponding DBQL statement, executes the DBQL statement directly against the database through a read-only connection, and returns relevant data to the end user within seconds.
AutoQL is a next generation conversational AI solution that enables seamless database exploration through natural language. Our robust API, customizable language models, and diverse implementation options guarantee unprecedented flexibility and value for our partners. Embeddable within any branded or proprietary software application, our open-source widgets make it possible to implement AutoQL wherever your users are already working. Alternatively, AutoQL can be accessed via our comprehensive web app (which houses all our available widgets in one place and is brand-customizable), or directly through integrations with Microsoft Teams and Excel.
AutoQL enables the dynamic generation of database query language from natural language
When it comes to accessing information from a business’ relational database, team members are typically required to run database query language like SQL and/or query against pre-pulled, non-comprehensive datasets. With AutoQL, users simply ask questions in their own words (natural language) to access and analyze their data, directly from the source.
When a user asks a question, their natural language statement is dynamically translated into a database query language statement and executed directly against your database in real time, returning accurate data responses in easy-to-understand visualizations, all within seconds.
AutoQL is a dynamic translation system, no intent classification technology is used
We rapidly generate custom training data that is unique to your database and business
To facilitate the dynamic translation of natural language to database query language, AutoQL’s proprietary machine learning models are trained on high volumes of training data that is custom-generated for each unique database. We facilitate the generation of training data through semi-automated techniques, cutting weeks off the integration process and ensuring any unique business logic and database complexities are accounted for.
Intelligent Language Models work together to enable conversational data experiences
Once training is complete, AutoQL’s core language model can dynamically generate comprehensive SQL statements from natural language queries.
AutoQL’s core translation model ensures high efficiency and accurate responses with the help of several auxiliary models. These models work together to facilitate seamless conversational data experiences for your users by:
- Allowing users to explore queries related to topics of interest
- Helping the system recognize and verify unique data labels
- Handling ambiguity or user-input errors
- Auto-suggesting relevant queries as users type
You offer data on demand to all your users
When AutoQL receives a natural language query input, a corresponding SQL statement is dynamically generated and a data response is returned to the user in real time. Our flexible implementation options make it easy for users to visualize data in different charts and graphs, or drilldown to access details in a single click. AutoQL enables businesses to deliver data on demand, empowering users to get important answers, instantly, and discover meaningful insights from anywhere, at any time.
AutoQL is enabled through the rapid generation of high-quality training data and several machine learning models that work together to elevate human-to-database interactions. Check out the links below for more info on each component of the AutoQL system:
Updated 9 months ago