LearningLoop
Continuous learning built-in
Phase 0 — CompleteNext: Phase 1 — Observability

Close the loop between experience and improvement

LearningLoop watches your AI workflows, evaluates outcomes, routes feedback to humans when it matters, and redeploys improvements safely. Your models, but less entropy.

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HITL controls
Drift detection
Safe rollouts

Observability

Telemetry for models & UX with smart sampling.

Evaluation Engine

Scores outcomes, flags anomalies, triggers action.

Learning Pipeline

Feeds labeled data to retraining jobs automatically.

Adaptation

Reconfigures agents and redeploys updates safely.

HITL

Reviewer workflows & SME gates for compliance.

MCP Ready

Orchestrates updates across agent meshes.

Drift Radar

Detects statistical drift with canary checks & alerts.

Audit-Ready

Every correction & redeploy is tracked with diffs.

Confidence Guardrails

Auto-escalate to humans when confidence dips.

How it works

Closed-loop system
LearningLoop closed-loop system: Observe → Evaluate → Learn → Adapt with Human-in-the-Loop

Live Metrics (demo)

Drift signal
12h
MTTD — Mean Time to Drift Detection
3d
MTTR — Mean Time to Retraining
94%
Capture — UX correction capture rate
+/-4%
Conf. — Confidence stability

Pricing

Simple tiers. Cancel anytime.

Starter

$49/mo
  • Telemetry SDK
  • Basic drift checks
  • Email alerts
Start

Growth

Popular
$149/mo
  • Active learning queue
  • HITL workflows
  • Rollout guardrails
Upgrade

Enterprise

Custom/mo
  • SSO & RBAC
  • PII scrubbing
  • On-prem options
Contact Sales

Request a demo

Give us a bit of context. We’ll follow up with a tailored walkthrough and a sandbox environment.

What happens next?

  1. We review your use case & map the observability hooks.
  2. We configure evaluation thresholds & drift checks.
  3. We enable HITL lanes and deploy a sandbox loop.
HITL Mode
Enabled
Canary Evals
Nightly
Quick links