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Run SQL for all your Unstructured Data

The Superinsight
Machine Learning Relational Database

Data is the most important aspect of AI. By using a Machine Learning Relational Database, you will be able to power all your applications, business intelligence software and visualization software with AI, get started with your early access account.

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Machine Learning SQL Database Capabilities

Performing ML at the data level enables AI functionalities for everything that follows

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Semantic Search

Semantic search features are included into our machine learning SQL database. Any text or image data types stored will be transformed to embeddings automatically, allowing you to do semantic search using a SQL select query.

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Pertained models are available by default so you can use to perform ML tasks such as text classification, question answering, text generation and translation. Models are available inside the database so you can make predictions from data in your database tables.

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Custom machine learning models can be trained directly with the data in your database using simple SQL query. After your custom model has been created, you can use preform predictions using simple SQL query.

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Adjustable Pricing

Pricing for storage and compute are separated. You only pay for computing costs when you need to conduct CPU or GPU operations. During idle hours, you will only be charged for data storage in your ML database. Simply add extra computational resources to your MLSQL database if you need to scale up your operations.

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Works with your favorite BI Tools

Because Superinsight is SQL-based, it will operate with your existing BI tools like as Tableau, Power BI, Preset, Looker, Sisense, and any other SQL-based data visualization tools. When you execute a SQL query using Superinsight on your BI tools, Superinsight will conduct ML operations and provide AI-enabled insights to your dashboard.

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Data Storage Integration

Since some of your data may be stored in different data warehouses, data lakes, or databases. Superinsight supports can read your unstrcutured data from existing data storage and index your data automatically. So there is no need to move your data around.

Data as a Product

Data is no longer a byproduct of business activities, it is the lifeblood of each organization

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By putting machine learning at the database level, every team can use AI with their data and create data product that will provide new insights for everyone in the organization.


Developers are the people who create applications for people both inside and outside of the organization. Every developer wants to build AI-enabled applications, but most are held back because the tools used in machine learning development are not user-friendly. Superinsight empowers developers by allowing them to perform ML operations using SQL queries. With this new approach, developers can focus on building features and incorporating AI functions for their users both inside and outside of the organization, assisting their organization in its transition to AI adoption.

Domain Experts

Domain experts understand their business domain and the true meaning of their data. Most domain experts today use data visualization tools like Tableau, Power BI, Sisense, and Superset to gain insights from their data, but these tools do not support AI. Because the Superinsight ML database is SQL-based, domain experts can now integrate Superinsight with their existing BI tools and perform ML operations within their BI tools. Domain experts will gain AI insights and become more informed about their business as a result of this new approach, allowing them to make better decisions faster.

Data Engineers

With the introduction of ML databases, different teams within an organization can now manage their own data product, allowing data engineers to focus on building the infrastructure that will allow the organization to orchestrate the flow of data.

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“Simply the best”

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John Carter
Investment Advisor
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“One of a kind app”

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“A game changer for us”

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Investment Firm

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