How AutoQL Works

Getting answers and information from a database should (and can) be as simple as having a conversation. So, we built AutoQLAutoQL - AutoQL enables the dynamic translation of natural language (NL) to database query languages. It is an embeddable AI-as-a-Service (AIaaS) that enables software vendors to deliver state-of-the-art conversational data experiences within their own applications. to help people access and interact with their data in a natural, intuitive way.

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AutoQL Infrastructure

Powered by a suite of proprietary Conversational AIConversational AI - Conversational AI is a form of Artificial Intelligence that allows people to communicate with applications, websites, and devices in everyday, humanlike natural language. For users it allows fast interaction using their own words and terminology. technologies, AutoQL is Chata’s natural language (NL) to database query language (DBQL) translation solution. Through our API, AutoQL receives, understands, and interprets the NL query input by the user, dynamically generates a corresponding DBQL statement, executes the DBQL statement against the database through a read-only connection, and returns relevant data to the end user within seconds.

Embeddable within any branded or proprietary software application, 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.

How It Works:

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. With AutoQL, users simply ask questions in their own words (natural language) to access and analyze their data.

When a user asks a question, their natural language statement is dynamically translated into a database query language statement and executed against your database in real time, returning accurate data responses in easy-to-understand visualizations within seconds.

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AutoQL is a dynamic translation system, no intent classification technology is used

We rapidly generate completely custom training data 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 your users, from your existing software or portal

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 software providers to deliver data on demand, empowering users to get important answers, instantly, and discover meaningful insights from anywhere, at any time.

AutoQL System

AutoQL is enabled through the rapid generation of high-quality training datatraining data - An initial set of data used to help a program understand how to apply technologies like neural networks to learn and produce sophisticated results. and several machine learning modelsmachine learning models - Machine learning (ML) models are model artifacts that are created by the training process. The process of training ML models involves providing an ML algorithm (that is, the learning algorithm) with training data to learn from. that work together to elevate human-to-database interactions. Check out the links below for more info on each component of the AutoQL system: