Everpure (P) Investor update summary

Data primacy and Data Intelligence

• AI is shifting IT from application-centric systems toward data primacy: shared context and reusable data across systems instead of copying data into separate applications.

• Data integration is estimated at 80%–90% of enterprise spend; data primacy aims to reduce integration and duplication, improve coherence and lower AI token costs.

• Data Intelligence discovers, classifies and contextualizes structured and unstructured data across cloud, on-premises, mainframes and storage platforms.

• Data Intelligence market estimated to grow from $6B to $21B by 2030; CIO, CISO and CDO involvement is broadening budgets.

• Examples: reducing a credit-card company’s DSAR response time from 21 person-weeks to under three minutes, and identifying 91M exposed card records at a payment processor.

Unified platform and AI capabilities

• Enterprise Data Cloud combines Evergreen architecture, a unified data plane and Intelligent Control Plane to unify workloads, apply data context and automate intent-based infrastructure operations.

• Fusion had 2,300 customer adopters, with adoption nearly doubling in the prior six months; Options Technology reported hundreds of staff hours saved monthly.

• Data Stream vectorizes enterprise data for RAG and governs vectors; key-value acceleration delivered 20x faster inference in cited testing.

• Evergreen has delivered nine hardware generations over 15 years without disruption; software innovation adds ~200 net new features annually.

• Data Intelligence supports visibility, governance, compliance and security use cases, with capabilities offered across different storage environments.

Neocloud and FlashBlade//EXA

• Training, inference and agentic workloads raise demand for GPU capacity; storage bottlenecks can limit utilization, with a third-party study finding AI clusters generally below ~85% utilization.

• FlashBlade//EXA separates metadata and data performance, scaling both independently for Neocloud workloads using open standards and without GPU-server software requirements.

• STN simplified its storage stack with FlashBlade//EXA and improved performance 3x and beyond; per-node throughput rose from 30–40 GB/s to 100 GB/s.

• STN reported 10%–20% faster end-to-end training and inference, including GPU time, alongside higher GPU utilization.

• The same core software and IP underpin enterprise, Neocloud and hyperscale offerings, with configurations tailored to different operating requirements.