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By transforming natural free-flowing query language into a structured query format, Natural Language Processing (NLP) allows question answering on a dataset. This is still one of the most challenging tasks in NLP, and it has gotten a lot of attention recently thanks to the availability of efficient language models.
The ability to convert natural language inquiries into Structured Query Language (SQL) has a wide spectrum of uses:
• Making data-driven insights available to those who don't know how to code
• lowering the time it takes to gain knowledge in a certain topic
• Increasing the value of the data that has been collected
As the amount of digital data has grown, a big amount of it has remained unanalyzed due to a lack of:
• infrastructure to keep it running
• processing techniques that are efficient
• There aren't enough individuals with the technical know-how to work with it. …

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OpenAI released GPT-3.5 to open-source community access for everyone this year.
Unlike most AI systems which are designed to focus on one specific use-case, the OpenAI API has multiple services that are able to perform different tasks.

There are really promising services as Parse unstructured data and Extract contact information web services. Available summarisation and text generation service is one of the best for OpenAI within the industry, but we will focus this review on AI system by GPT-3.5 that translates natural language to SQL.

OpenAI SQL Query generation based on Natural Language input AI turned our inaccurate for a really simple question "to find how many users in the USA?". We've posted a few screenshots with GPT-3.5 results. Also, we've attached accurate results for the same question from our NLSQL software.

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Amazon QuickSight Q vs NLSQL is always a crucial battle for empowering employees with data analytics. NLSQL provides plenty of database integrations and user interfaces, likewise MS Teams, Slack, self-service NLP to SQL API, custom web or mobile app chat support, etc. On the other hand, Amazon QuickSight Q is well focused on Redshift and Amazon QuickSight's front-end interface.
Do you know that in the past, business professionals relied on the data analytics department to generate any report that was generated from the company database? However, with the development of NLSQL and Amazon QuickSight Q, employees can now build any report using only natural language questions.

Both of these tools provide a variety of features, strength, and also have their own merits and demerits. But these tools are growing faster as the tools created for solving challenging problems of natural language understanding and SQL code generation. In order to solve …

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NLSQL, the pioneer in intelligent, conversational, and Business Intelligence solutions, has joined Microsoft for Entrepreneurs, a worldwide initiative devoted to speeding the scale for high-potential startups.NLSQL will have unique access to Microsoft's technology, market ecosystem, and business assistance as a program participant, as well as the chance to work with other program partners.

Trusted by the world’s leading brands, NLSQL empowers employees with an intuitive text interface to poorly accessible corporate data to inform and speed data-driven business decisions, bringing enormous competitive advantages to our customers. NLSQL is used by marketing, sales, and data analytics teams to make proper business decisions, which drives businesses forward faster. NLSQL platform allows employees to chat with complex databases via intelligent MS Teams/Slack applications on mobile or desktop devices.

NLSQL will migrate and use Microsoft Azure in a full to host its expansive infrastructure, providing built-in redundancy and security. NLSQL is powered by Azure …

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Self-Service Business Intelligence (BI) is an approach to data analytics that enables business users access and explore data sets. With traditional BI tools and processes, the BI team or IT does the data analysis work for business users. In a self-service BI environment, users can run queries themselves and create their own data visualizations, dashboards and reports.

The main challenge is quick adoption by business users. Like traditional BI environments, self-service ones can be held back by resistance from business executives and managers who want to continue to base decisions on their own knowledge and intuition. Meanwhile, the adoption of Self-Service BI tools could nail bad or no data issues and as a result force additional budget allocation for cleaning data and proper business process design and employees training.

To avoid or overcome such obstacles, a company must first develop a well-thought-out BI strategy, which includes a sound BI architecture …

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Download Case Study with Delta Airlines (pdf ppt)