AI & Machine Learning
Real-Time Context for AI Systems
AI systems are only as good as the context they receive. Whether serving features to ML models, providing memory to AI agents, or powering RAG pipelines, real-time context determines the quality of every prediction and decision.
Real-time
Feature serving
Scalable
Feature storage
Point-in-time
Consistency
Unified
Vector + structured
The context bottleneck
Modern AI systems face a fundamental bottleneck: models are increasingly capable, but the infrastructure to serve them context has not kept pace. Feature stores are batch-oriented. Vector databases are isolated. The result is AI that decides with stale or incomplete information.
AI agents compound this problem. They need to maintain memory across interactions, coordinate with other agents, and act on shared state—all within the latency constraints of real-time applications. Traditional architectures cannot support this.
Tacnode Context Lake is purpose-built for AI context. It unifies feature serving, vector retrieval, and agent memory into a single real-time layer—ensuring that every model and agent operates on the freshest, most complete context available.
Key Use Cases
How leading ai & machine learning organizations use Tacnode
Real-Time Feature Serving
Serve ML features at prediction time with millisecond latency and guaranteed consistency with training data.
AI Agent Memory
Provide AI agents with shared, persistent memory that enables coordination and learning across interactions.
RAG Pipeline Acceleration
Power retrieval-augmented generation with real-time vector search combined with structured context.
Model Monitoring
Track model performance and detect drift in real-time by correlating predictions with ground truth signals.
Technical Specifications
Capabilities and compliance
Capabilities
Compliance
Frequently Asked Questions
Common questions about AI & Machine Learning
Related Resources
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Why Tacnode
The context layer for AI
AI systems require context that is fresh, complete, and consistent. Tacnode Context Lake provides the unified context layer that enables real-time feature serving, agent memory, and RAG at the speed of inference.
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