ai

AI success doesn’t start with models, it starts with data. Learn why unstructured data preparation is the most critical (and overlooked) step in building effective AI pipelines.

AI success doesn’t start with models, it starts with data. Learn why unstructured data preparation is the most critical (and overlooked) step in building effective AI pipelines.

AI can write code – but can it build a real product? See how Diskover’s VP of Product used AI agents to define requirements, generate Jira stories, implement features, and ship an MCP connector that’s headed to customers.

AI is driving unprecedented demand for memory and storage, creating a growing infrastructure crunch that threatens both AI initiatives and everyday business operations. As supply tightens and costs rise, organizations must focus on using memory and storage more efficiently to protect ROI, delay costly upgrades, and keep critical workloads running.

Most enterprises are racing toward AI, but the majority of their data simply isn’t ready. Siloed storage, missing metadata, manual workflows, and years of cluttered unstructured files slow AI initiatives before they start. This post explores why AI-readiness is so hard – and how Diskover helps organizations finally structure the unstructured.

Enterprises are generating unstructured data at massive scale, but traditional tools can’t deliver the visibility or automation needed to make that data AI-ready. In this summary of our joint NetApp + Diskover post, we highlight how ONTAP’s secure foundation and Diskover’s global indexing, enrichment, and policy-driven workflows help organizations unlock the true value of their data—at speed and at scale.

AI natural language interfaces are redefining how we interact with data. Instead of navigating complex dashboards or waiting on reports, users can simply ask questions and get instant insights. As unstructured data grows and AI fluency spreads, natural language is becoming the new interface for discovery—bridging the gap between human understanding and machine intelligence.

The CloudSoda AI Data Assistant began as an experiment with Anthropic’s Model Context Protocol (MCP) and quickly became a working prototype unveiled at the NAB Show. Built to analyze storage, simulate cost savings, and surface duplicates, it gave both engineers and executives instant, actionable answers in plain language.

Diskover VP of Product Olivier Rivard shares how Anthropic’s Model Context Protocol (MCP) transformed his AI experiments into real product ideas — and what it means for the future of unstructured data management.