For the vast majority of tasks that don't require deterministic handling, Learned Routing uses historical performance data to optimize how requests are processed. It learns which memory pack combinations produce the best results for different task types, which models are most effective for specific domains, and how to balance latency against quality for different priority levels. The routing improves continuously as the system processes more work.
Routing decisions are evaluated against actual outcomes. Successful routes are reinforced while underperforming routes are adjusted — creating a continuous improvement cycle.
Different tasks have different urgency. The router balances response quality against speed, using faster configurations for routine tasks and more thorough processing for complex ones.
Across multiple models and processing resources, the router distributes work for optimal throughput and reliability.
The router can run controlled experiments, comparing different routing strategies to identify improvements before committing to them system-wide.
Learned Routing works in concert with other layers in the intelligence stack — each connection amplifying the capability of both components.
Continuously optimize AI performance without manual tuning. Learned routing ensures your AI systems improve over time — delivering better results, faster responses, and more efficient resource utilization as usage data accumulates.
Discover how Learned Routing fits into your enterprise intelligence strategy.
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