Feature Store
Compute and serve features directly from shared, live data—so every decision evaluates against up-to-date, consistent feature values.
The problem
Feature stores are meant to serve features at decision time. Most fall short once systems become real-time and concurrent.
What breaks down:
- Features lag behind live data
- Different services observe different feature values
- Freshness depends on complex batch and sync pipelines
- Correctness relies on coordination outside the data system
The result isn't crashes or outages — it's feature drift. Decisions are made on values that no longer reflect the current state of the system.
How Tacnode solves it
Tacnode computes and serves features directly on top of live data.
How it works:
- Features are defined as deterministic transformations over streaming and transactional inputs
- Feature values update incrementally as data changes
- Updated features become available immediately for decision evaluation
Because computation and serving happen inside a single system:
- Feature values stay aligned with live data
- All consumers observe the same results
- Consistency is enforced by the system, not application logic
No batch jobs. No offline pipelines. No external caches.
Key Capabilities
Online Feature Generation
Derive features continuously from live data as it changes.
Semantic Feature Logic
Apply embeddings, similarity, and semantic predicates directly in feature definitions.
Consistent Feature Serving
Serve the same feature values to all services and agents evaluating decisions.
Decision-Time Evaluation
Evaluate features against a coherent snapshot of state at the moment a decision is made.
How it works
Architecture Highlights
- Incremental feature derivation from live data
- Decision-time evaluation against consistent state
- Native semantic operations in feature logic
- No batch pipelines or external synchronization
Use Cases
Personalization
Adapt recommendations and ranking based on live user behavior and signals.
Fraud and risk decisions
Evaluate risk features against the most recent activity without delayed updates.
Real-time optimization
Continuously adjust decisions as inputs change, without rebuilding features offline.
Capabilities
- Native embeddings
- Transactional similarity search
- Decision-time serving
Integrations
- ML frameworks
- Vector operations
- Real-time inference
Documentation
Collective intelligence for your AI systems.
Enable shared, live, and semantic context so automated decisions stay aligned at scale.