FEED HIGH-VALUE DATASETS TO YOUR AI AND BI PIPELINES

When even AI thinks it’s too much data—we make sense of it.

Diskover bridges AI and BI pipelines with complete data visibility and control—ensuring only the most relevant, high-quality data flows into your models. By enriching unstructured data with deep metadata context, Diskover turns overwhelming complexity into actionable intelligence for analytics and machine learning.

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DISKOVER MAKES YOUR DATA AI-READY

Transforming disorganized data into AI/BI-ready intelligence.

Diskover turns fragmented, unstructured data into well-organized, high-context datasets that power stronger LLMs, smarter models, and more reliable outcomes — ensuring AI learns from the best, not just the most.

Context.

Rich catalog of core, business, and system metadata that gives data meaning and traceability.

CONNECT

Content.

Unstructured data enriched with the who, what, where, and when to complete the story.

Get your metadata
RAG-ready.

Curate data with precision to surface only what’s relevant for Retrieval-Augmented Generation.

OPTIMIZE DATA QUALITY

Marry content to context.

Combine deep metadata with content awareness to deliver cleaner, more intelligent training data.

Dataset curation on steroids.

Streamline data discovery, validation, and lifecycle management for better AI inputs.

ENHANCE LLM TRAINING

Build better, faster models.

Train on curated, contextualized data to enhance performance, reduce bias, and cut training time.

DRIVING CONTEXT, SPEED, AND HIDDEN VALUE FROM UNSTRUCTURED DATA

Metadata—the fuel for AI.

Metadata-derived attributes power machine learning models, enrich input data, improve prediction accuracy, and uncover deeper relationships that traditional methods often overlook.

Contextual understanding.
Metadata adds key business details—like source, owner, and purpose—helping teams and AI interpret data accurately and turn context into real insight and faster, better outcomes.
Filtering and categorization.
Metadata organizes unstructured data by business unit, value, or project, streamlining retrieval and accelerating time-to-insight across large enterprise datasets.
Enhanced data quality.
Detecting gaps or inconsistencies in metadata strengthens data integrity across systems, ensuring analytics and governance decisions are based on reliable information.
Unlocking hidden value.
Metadata connects unstructured data to its business relevance, revealing insights that drive innovation, efficiency, and competitive advantage organization-wide.

REAL-WORLD EXAMPLES

Why it matters.

Metadata transforms unstructured data into meaningful context—unlocking hidden patterns and accelerating AI-driven innovations.

Document analysis.

Use metadata such as author, department, and project to classify documents efficiently—simplifying compliance reviews and enterprise knowledge management.

Image recognition.

Metadata such as camera settings, geolocation, and capture context enhances image tagging, training accuracy, and digital asset organization across business workflows.

Genomics data analysis.

Diskover’s BAM metadata plugin extracts details such as sample ID, sequencing platform, genome build, read group, and alignment stats—streamlining the filtering and organization of large datasets to accelerate research, improve reproducibility, and deliver high-quality data to AI/ML pipelines.

Energy data analysis.

We bring together fragmented datasets from exploration, drilling, and production—creating unified, metadata-rich views that feed directly into AI and BI pipelines— delivering faster, data-driven insights that improve operational efficiency and strategic decision-making.

DISKOVER FEEDS CURATED DATASETS TO YOUR AI MODELS

How we do it.

We bring data clarity and precision.

Index

Discover and inventory data across all sources—on-prem, cloud, or hybrid—to establish a single searchable view.

Enrich

Harvest and correlate metadata from files, systems, and business apps to provide context and meaning.

Curate

Organize, classify, and prepare high-value datasets using policy-driven workflows and governance.

Feed AI Pipelines

Deliver structured, context-rich datasets to analytics and AI tools for faster insights and smarter automation.

Data indexing and discovery.
We scan 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.
Metadata enrichment.
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.
Lineage and provenance tracking.
We track 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.
Optimization through powerful analytics.
We identifiy 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..
Compliance and governance.
By leveraging extensive metadata and targeted queries, we ensure 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.
Diagram illustrating how Diskover curates and structures unstructured data into metadata-rich datasets for AI and BI pipelines. It shows how Diskover integrates with Elasticsearch and OpenSearch to prepare relevant datasets that power smaller AI models and Retrieval-Augmented Generation (RAG) systems—ensuring clean, high-quality data for more accurate and efficient AI results.
Visual overview showing how Diskover unifies unstructured data across the asset lifecycle—from capture and enrichment through transformation, assembly, and delivery. The diagram highlights how metadata-driven indexing, curation, and agentic workflows streamline data movement from raw files to AI and BI consumption layers such as data warehouses, vector databases, and LLM endpoints.

We tame the complexity of unstructured data.

AI challenges with unstructured data

Today’s most powerful AI models are built on decades of structured, well-labeled training data. But to unlock the next frontier of innovation, organizations must shift their focus to what has long been the most elusive source of insight: unstructured data.

Managing, curating, and preparing this data for AI pipelines is no small task. It’s a multidimensional challenge—spanning scale, complexity, and context. But when approached strategically, unstructured data can become one of your greatest assets.

Diskover helps you understand, enrich, and organize unstructured data across your entire estate—accelerating AI workflows, shortening time to value, and optimizing output quality.

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Community Edition on GitHub

A free edition with no time limit available on GitHub.

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