SOLUTIONS for AI PIPELINES

Transforming disorganized data into relevant, actionable datasets.

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Context.

Rich catalog of base and business-context metadata.

Content.

Missing the what, where, when, and how for unstructured data.

Get metadata
RAG-ready.

A powerful search engine to find and catalog relevant datasets and help sort files that matter.

Marry content
to metadata.

Vast contextualized quality data combined with introspection to train LLMs and reduce irrelevant data training.

Data curation
on steroids.

Vastly simplified human queries and lifecycle management.

Build better
models.

Focus and train models to use metadata context.

Diskover scans and indexes vast amounts of unstructured and structured data across various storage systems. This comprehensive indexing enables organizations to locate and access relevant data swiftly, a critical step for feeding accurate and diverse datasets into AI and BI models.

By harvesting and enriching metadata, Diskover adds context to datasets. This enriched metadata facilitates better data classification and tagging, improving the quality of data inputs for AI algorithms and enhancing the precision of BI analytics.

Diskover tracks data lineage, providing insights into data origins and transformations. Understanding data provenance is vital for training reliable AI models and ensuring the integrity of BI reports.

Diskover identifies redundant or outdated data, allowing organizations to streamline their datasets and focus on relevant, high-quality information. This ensures that AI and BI systems work with accurate, up-to-date data, enhancing the precision of analyses and predictions.

With features that support data compliance and governance, Diskover ensures that data used in AI and BI pipelines adheres to regulatory standards. This compliance is crucial for industries with strict data handling requirements, such as healthcare and finance.

Diskover curated datasets in the AI pipeline diagram.
The benefits of Diskover's curated datasets in the AI pipeline workflow.

Metadata provides additional details about the source, creation date, author, and other relevant attributes, which helps AI models interpret the raw data more accurately. 

Identifying potential inconsistencies or errors in metadata can help improve the overall quality of the unstructured data used in AI models. 

Extracting metadata like user location, sentiment, and post time from social media posts to gain deeper insights into public opinion. 

Metadata allows for faster and more precise retrieval of specific data points within a large dataset of unstructured information. 

Unstructured data often holds valuable insights that may not be readily apparent without extracting relevant metadata. 

Extracted metadata can be used as additional features in machine learning models, enriching the input data and improving prediction accuracy. 

By extracting metadata like file type, document category, or subject, AI systems can efficiently filter and categorize unstructured data, enabling targeted analysis. 

Extracting metadata like document title, author, and creation date from a PDF file to categorize and prioritize documents for analysis. 

Using metadata like location, camera settings, and date taken to improve the accuracy of image classification. 

AI challenges with unstructured data

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Community Edition on GitHub

A free edition with no time limit available on GitHub

Community Edition on AWS

A free edition with no time limit available on AWS Marketplace


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