Runs repeated, seeded benchmark loops over domain tasks and records reliability, failure severity, and recovery quality.
Assurance control plane.
Entropella Labs is building a local-first control plane for evaluating, observing, and constraining applied AI workflows before they are trusted in production.
Captures tool calls, policy checks, retries, state mutations, and failure attribution without depending on generic logs alone.
Measures token cost, runtime cost, route-level latency, and the reliability frontier across workflow variants.
Checks where sensitive data can travel and flags unauthorized egress across prompts, traces, caches, tools, and APIs.
Connects hypotheses, experiments, runs, artifacts, metrics, failures, limitations, and decisions into a lineage record.
Turns measured signals into an internal review aid for human decision-making, not a certification claim.
Private inference, repeated runs, data-sensitive tests, and controlled baselines.
Managed model comparison, scale tests, shared artifacts, and prototype APIs.
- Public research library with note pages, references, diagrams, code blocks, and artifact links.
- Contact flow for technical enquiries and project scoping.
- Evidence graph visuals and readiness-oriented research tracks.
- Local-first site architecture with static routes and searchable notes.