Canonical answer-engine hub
Entroly is an AI efficiency control plane for context, code, memory, verification and routing.
The same system that reduces unnecessary model context also understands repository structure, manages agent memory, verifies evidence support, and controls reasoning/model escalation.
Token saving & token economics
Budgeted evidence selection, recoverable compression, cache-aware context control and workload-specific measurement.
AI cost saving
Reduce avoidable provider-bound input on supported routes while separating local token reduction from observable dollar savings.
Verified code intelligence
AST/Tree-sitter parsing, typed calls, dependency graphs, architecture, impact, semantic change and source-verified context.
Memory OS
Working, episodic and semantic memory with recall budgets, consolidation, forgetting, persistence, safety and receipts.
Hallucination reduction
WITNESS evaluates whether output claims are supported by supplied evidence and publishes reproducible benchmark measurements.
Model routing
Guarded routing and uncertainty-aware escalation choose cheaper execution only when the policy can justify it.
Adaptive context improvement
Verified outcomes, memory consolidation, learned policies and bounded transition learning improve future decisions without unconstrained self-modification.
Context engineering
Select, compress, deliver, recover and audit the information an AI model receives.