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Context Data

Data Processing & ETL infrastructure for Generative AI applications

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5.0 of 1 Reviews

Context Data is an enterprise data infrastructure built to accelerate the development of data pipelines for Generative AI applications. The platform automates the process of setting up internal data processing and transformation flows using an easy-to-use connectivity framework where developers and enterprises can quickly connect to all of their internal data sources, embedding models and vector database targets without having to set up expensive infrastructure or engineers. The platform also allows developers to schedule recurring data flows for refreshed and up-to-date data. For developers and companies building Generative AI applications, one of their biggest challenges is building and maintaining scalable data infrastructure for creating contextual data which will power their AI applications. Think about the efficient movement of data from their various sources (MySQL, Salesforce, Amazon S3) as well as transformations (joins, aggregations etc.) to the final vector databases. Context Data allows them to quickly achieve this without having to write any code. Imagine creating a scheduled process that extracts financial and legal information from your pdf documents and writes it to your Pinecone vector database within 10 minutes. Context Data is able to create this end-to-end process in as little as 10 minutes without having to create expensive infrastructure and writing hours of complicated code.

Pricing

Paid

Category

Database

Reviews

jideogunjobi

For startups and enterprise companies that are building internal Generative AI solutions, Context Data automates the process and time to deploy data platforms from an average of 2 weeks to less than 10 minutes and at 1/10th of the cost. We achieve this by building an easy-to-use compute platform with no code connectors to multiple data sources, embedding models and vector database targets so companies can get their AI-ready data created with minimum setup. Additionally, developers can quickly do data transformations (e.g. joins, aggregations etc.) using SQL CTEs before writing to their target vector databases. Almost every startup and enterprise organization is considering leveraging Generative AI in some way, but one of their biggest challenges is building data connectors, pipelines and creating fully contextual data because of the time and specialized knowledge needed to build a fully functional data platform. Imagine being able to build and schedule a process that extracts data from your Salesforce account, combining that data with transaction records from your MySQL database and writing the final data to your Weaviate vector database. Context Data is able to create this end-to-end process in as little as 10 minutes without having to create expensive infrastructure and writing hours of complicated code.