From Storage to Enterprise Intelligence, Unlock AI Value from Private Unstructured Data with CTERA

Discover the obstacles that hinder AI adoption. What matters most? Data quality or quantity? Understand the strategy CTERA uses for curating data to create trustworthy, AI-ready datasets that overcome silos and security challenges, translating raw data into meaningful and actionable insights.

CTERA’s presentation at AI Infrastructure Field Day 3 focused on the transition from traditional storage solutions to “enterprise intelligence,” highlighting the potential of AI to unlock value from unstructured data. While enterprise GenAI represents a massive market opportunity, with projections reaching $401 billion annually by 2028, the speaker, Aron Brand, emphasized that current adoption is hindered by the poor quality of data being fed into AI models. Brand argued that simply pointing AI tools at existing data leads to “convincing nonsense,” as organizations often lack understanding of their own data, resulting in inaccurate and potentially harmful outputs. He identified three main “quality killers”: messy data, data silos, and compliance/security concerns.

To overcome these obstacles, CTERA proposes a strategy centered on data curation, involving several key steps. These include collecting data from various storage silos, unifying data formats, enriching metadata, filtering data based on rules and policies, and finally, vectorizing and indexing the data. CTERA aims to provide a platform that enables users to create high-quality datasets, enforce permissions and guardrails, and deliver precise context to AI tools. The platform is powered by an MCP server for orchestration and an MCP client for invoking external tools, facilitating an open and extensible system.

CTERA’s vision extends to “virtual employees” or subject matter experts created by users to automate tasks and improve efficiency. The system respects existing access controls and provides verifiable answers grounded in source data. The presented examples demonstrated the potential of the platform in various use cases, including legal research, news analysis, and medical diagnostics. The presentation emphasized that the goal is not to replace human workers but to augment their capabilities with AI-powered assistants that can access and analyze sensitive data in a secure and compliant manner.