Accessible
Can the right data be accessed in the right way by AI systems?
The Shift
AI workloads have specific requirements: vectors for RAG, features for inference, real-time access for agents. Data must be available in the right shape, at the right latency, without manual transformation.
Requirements
What must be true about the data itself.
- Data available in formats AI workloads require
- Access patterns support AI latency requirements
- Same data servable to multiple consumer types without duplication
Capabilities
What your infrastructure must support.
Multi-modal storage
Access to structured, semi-structured, and unstructured data
Vector storage and similarity search
Native support for embeddings, ANN/KNN search
Feature store / feature serving
Consistent, low-latency access to ML features across training and inference
Real-time data serving
Low-latency, high-concurrency reads for inference and agents
Universal data access layer
Single interface for diverse consumers (BI, ML, agents) without duplication