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In today’s fast-paced business environment, staying ahead of the curve is essential for success. One way to achieve this is by leveraging the power of AI-driven data analytics and anomaly detection. At NLSQL, we are committed to helping businesses harness the power of AI to make better-informed decisions, and we are excited to announce the launch of our new standalone product, AI Anomaly Detector.

Employees often face the daunting task of finding valuable insights hidden within vast amounts of data, akin to finding a needle in a haystack. This challenge can be overwhelming and time-consuming, especially when anomalies are not immediately obvious. To address this challenge head-on, we developed AI Anomaly Detector, a powerful tool that works alongside our existing AI Data Analytics offerings.

AI Anomaly Detector: Proactively Identifying Data Anomalies AI Anomaly Detector is designed to perform regular anomaly checks on your data. Upon detecting any anomaly, it notifies …

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Try Anomaly Detector on Azure Marketplace

Intelligent Apps and AI-powered language models have become essential tools for large enterprises to streamline analytics and automate tasks. However, with the increasing reliance on these technologies, concerns about data security and the protection of sensitive information have also grown. In this blog post, we will discuss the main security concerns associated with NLSQL usage in large enterprises and how a secure software architecture on Azure, incorporating the NLSQL API service, can help overcome these concerns.
Main Concerns about Confidential and Sensitive Data Transfer are related to sensitive or confidential information transfer outside the corporate IT ecosystem for proper use based on your company use case.

To address these concerns, a secure software architecture can be implemented using Azure and the NLSQL API service. This approach offers several advantages:
1. No sensitive data transfer: With the NLSQL API service, there is no need to transfer sensitive or confidential data outside …

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In this blog post, we compare the data analytics capabilities of NLSQL and ChatGPT-4o using a dataset of import-export trade balance transactions from the US worldwide by product groups. We used an open dataset from the census, covering the years 2008-2023. Our test began by sending a full dataset as a CSV file to the latest ChatGPT-4o model and asking the question, "What are the top 5 products in export to China last year?"

ChatGPT-4o Response: The ChatGPT-4o response can be seen in the screenshot provided in this blog post. The top 5 products from highest export to lowest were:

1. Civilian aircrafts, engines, equipment, and parts
2. Semiconductors
3. Industrial Machines
4. Passenger cars
5. Crude Oil

NLSQL Response: NLSQL responded with a different set of top 5 products, which is actually correct and can be verified using the original CSV file filters. The top 5 products according to …

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NLSQL Open Demo containing import-export trade balance transactions worldwide

In this blog post, we will compare the data analytics capabilities of NLSQL and ChatGPT-4o using the same dataset and questions. The dataset used for this comparison is an open dataset from the US Census, containing import-export trade balance transactions worldwide by product groups between 2008-2023.

We began by sending a CSV file containing the full dataset to the latest ChatGPT-4o model. The first question asked was, "What is Canada crude oil import last year?". ChatGPT-4o replied that there was no data for crude oil imports by Canada for the latest year in the provided dataset. However, this was incorrect, as the data was present in the CSV file. We then asked the same question to NLSQL.
NLSQL bot responded, "Thank you for your query. As per the data available, the import of crude oil from Canada to the USA was 92531.63 million USD in the last year." This answer …

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NLSQL Open Demo containing import-export trade balance transactions worldwide

In today's competitive business landscape, organisations must continually adapt and evolve to stay ahead of market trends and customer demands. One key strategy to achieve this is by leveraging advanced analytics tools and platforms to harness customer data, improve insights, and make informed decisions regarding product assortments, marketing campaigns, and operational efficiencies.

Data-driven decision making (DDDM) is the practice of basing decisions on data analysis rather than intuition or observation alone. This approach enables organisations to make more informed decisions, as they can identify patterns, trends, and correlations that might not be immediately apparent. By leveraging advanced analytics tools, businesses can gain a deeper understanding of their customers, products, and operations, leading to improved decision making at all organisation levels and a competitive edge in the market.

Adoption of artificial intelligence (AI) and machine learning (ML) in analytics: AI and ML technologies are becoming increasingly prevalent in advanced analytics tools, …

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Discover NLSQL use case for Retail