Feature
See how AI is used across your organization, find recurring workflow friction and unusual activity, and open the sessions that explain every finding.
Enterprise layer
See AI work captured from native agent hooks, browser conversations, local proxy traffic, and the enterprise gateway in one organization view.
Connect usage to the team, project, developer, model, provider, and capture source that produced it—not just a provider total.
Find recurring patterns such as restart churn, repeated file discovery, context bloat, duplicated work, policy drift, and model–task mismatch.
Review unusual usage, first-seen technology, and runaway sessions with severity, status, occurrence history, and linked evidence.
Evidence chain
Aggregate dashboards can show where usage changed. ReclaimLLM preserves the organization, team, project, member, model, provider, and capture source behind it.
Signals and anomaly reports add an investigation path: see what matched, inspect the evidence sessions, and decide what needs enablement, policy, or no action.
Differentiation
ReclaimLLM normalizes sessions from coding agents, browser assistants, API proxy traffic, and the enterprise gateway so teams can investigate AI work across their actual tool stack.
Signals surface patterns that aggregate charts miss: repeated restarts, redundant discovery, oversized context, duplicated work, onboarding gaps, and avoidable model cost.
Deterministic detectors state what matched and how to interpret it. Admins can open the linked sessions before acknowledging, resolving, dismissing, or routing a fix.
Workflow
01
Normalize sessions from native hooks, browser conversations, local proxy traffic, and the enterprise gateway.
02
Connect activity to the organization, team, developer, project, model, provider, and capture source.
03
Surface recurring workflow Signals and deterministic anomalies from completed session evidence.
04
Open the evidence, choose the right owner, and acknowledge, resolve, dismiss, or route the finding.
Investigation coverage
The Signals matrix groups recurring AI-work patterns by team and project, showing projected weekly tokens when available and open finding count when not.
Anomaly reports run deterministic nightly checks for unusual usage, new technology fingerprints, and runaway sessions—with interpretation guardrails for every detector.
Connect organization-wide usage to recurring workflow Signals, deterministic anomalies, and the exact sessions your admins need to review.