Platform

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.

Scope
Reliability, observability, privacy.
Mechanism
Repeated runs, traces, evidence.
Outcome
Reviewable deployment decisions.
System view
01Research question
02Technical uncertainty
03Experiment protocol
04Repeated runs
05Artifacts and traces
06Metrics and failures
07Evidence graph
08Human review
09Decision record
10Archive evidence
Evaluation Harness

Runs repeated, seeded benchmark loops over domain tasks and records reliability, failure severity, and recovery quality.

Agent Trace Layer

Captures tool calls, policy checks, retries, state mutations, and failure attribution without depending on generic logs alone.

Cost & Latency Profiler

Measures token cost, runtime cost, route-level latency, and the reliability frontier across workflow variants.

Privacy Boundary Tester

Checks where sensitive data can travel and flags unauthorized egress across prompts, traces, caches, tools, and APIs.

Evidence Graph

Connects hypotheses, experiments, runs, artifacts, metrics, failures, limitations, and decisions into a lineage record.

Deployment Readiness

Turns measured signals into an internal review aid for human decision-making, not a certification claim.

Local and cloud split
Local

Private inference, repeated runs, data-sensitive tests, and controlled baselines.

Cloud

Managed model comparison, scale tests, shared artifacts, and prototype APIs.

What is implemented
  • 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.