Key Takeaways

  • Enterprise AI adoption stalls because internal training data sits trapped across twenty years of disconnected databases and unmapped cloud storage.
  • Business unit leaders block direct access to live databases to protect system stability and prevent leaks of sensitive records like executive salaries.
  • Eon co-founders Ofir Ehrlich and Gonen Stein show that converting passive backup data creates a safe, low-cost training pipeline for LLMs.
  • Automated classification and semantic mapping across hyperscalers remove personally identifiable information before models touch historical records.

The Twenty-Year Cold Storage Bottleneck

Every enterprise wants autonomous AI agents, but models cannot learn in a sterile test lab. They need real operational history. The problem is that most corporate data lives in forgotten silos. As Ehrlich pointed out on No Priors: “I have a lot of people working for me. They have data in multiple systems for the last 20 years. Some of them system that no one really understands where they contains production data because sensitive information.”

When data teams attempt to connect large language models to internal systems, they immediately hit resistance from department heads. A finance director will not hand over direct database credentials if doing so risks exposing payroll data or crashing customer-facing services. Stein noted that organizations sit on vast amounts of historical context, but “it's kept in their environment in different forms but it's locked. It's not accessible and usually it's very very expensive.”

Querying active production databases at scale creates latency, drives up computing bills, and introduces governance liabilities. If an autonomous agent runs an unindexed query across twenty years of transactional records during peak hours, it can take down core business infrastructure.

Turning Disaster Recovery into an AI Foundation

Instead of risking production stability, companies can unlock value by repurposing their disaster recovery archives. Backup systems already capture full historical snapshots across hyperscalers. The challenge has always been that backup archives exist as unstructured, unindexed storage blobs designed solely for emergency restores.

Ehrlich and Stein built Eon to turn these static backups into an active data foundation. As Stein explained: “We're able to take what customers already have, convert it into this new data foundation format that's much more stored much more efficiently and provide the mapping, classification, access control, and connect it into the AI workflows.”

By applying automated discovery and semantic mapping directly to backup snapshots, companies can classify every field before an AI agent touches it. PII, confidential executive compensation, and compliance-restricted columns get masked automatically. Because the agent reads from the converted backup layer rather than live infrastructure, engineers can run heavy data extraction jobs without degrading customer-facing applications. Ehrlich summarized the mandate for modern data leaders: “We can help you find all the data that's in organization in a very simple way, understand what it is, classify it, map it, understand context layer on top of that, build a semantic layer.”

What to Do With This

Audit your company's backup archives this week to identify historical datasets that your product team has avoided touching due to production risk. Write a pipeline script that masks customer PII on a restored staging snapshot, then benchmark your internal model on that masked historical data rather than testing on live databases.