AI That Remembers
When to Think
Adaptive AI infrastructure for efficient, governed decision-making
Shirley OS uses persistent memory and adaptive compute routing to determine when AI should reason, retrieve, or remain idle—optimizing cost and governance by design.
Decide when intelligence is needed.
Not every decision requires a full model.
Shirley OS lowers inference cost, latency, and energy use. Most requests resolve through memory. Full inference is the exception.
Persistent memory, not prompts.
Decisions informed by outcomes, not just tokens.
Deal Memory Graph™
Shirley OS uses a persistent memory layer—the Deal Memory Graph™—to store context, outcomes, and confidence over time.
Instead of treating every interaction as new, the system learns from prior decisions and applies that knowledge forward. When a new request arrives, Shirley evaluates memory first before deciding whether deeper reasoning or model escalation is necessary.
This enables consistent, auditable decisions while reducing unnecessary compute and operational cost.
Governance by design.
A control layer for memory, compute, and decision escalation.
Efficiency by design.
Reducing energy use by avoiding unnecessary inference.
Large models are powerful—but expensive.
Shirley OS resolves most requests through memory and lightweight reasoning, escalating to full inference only when needed.
This lowers cost, latency, and energy use by design.
