Prove incidents before action

An AI runbook executor that gathers evidence, runs safe diagnostics, and asks before production changes.

Get startedView workflow
  • Evidence packet
  • Sandbox replay
  • Human approval

Risk score

Checkout incident

High
82/ 100

Based on logs, metrics, and latest deploy.

Risk gauge at 82 percent82%
SafeRisky

Evidence trail

Runbook: checkout-failure

Alert received

Checkout error rate increased on payment-service.

Evidence gathered

Logs, deploy history, and metrics agree on one likely cause.

Sandbox check

Diagnostic script reproduced timeout failure in isolation.

Approval required

Rollback stays locked until an engineer approves it.

Approval gate

Action: rollback payment-service

Sandbox output
timeout_ms=3000
failed_requests=47
likely_commit=8f31c2b
recommendation=rollback
The loop

From noisy alert to approved run.

RunProof turns a page into a staged workflow: gather the signal, replay the failure, and approve the action with proof attached.

  • Incident packet ready before the first production step01
  • Human approval visible at the exact decision point02
  • Sandbox diagnostics attached to every recommendation03
  • Audit history that survives handoff and retrospectives04

Current gate

Rollback payment-service stays locked until an engineer approves the evidence packet.

Platform

A control layer for AI-assisted operations.

Use agents for investigation and diagnosis while keeping the production boundary explicit.

Evidence graph
Link alerts, deploys, logs, traces, and runbook rules into one reviewable packet.
Sandbox replay
Run diagnostics against an isolated target before the system recommends a production step.
Approval gate
Keep high-risk actions locked until an operator reviews the evidence and approves the run.
Audit trail
Record the recommendation, reviewer decision, and action state for incident review.

Policy-aware by default

Recommendations are useful only when the evidence is visible.
  • No production action runs without approval
  • Sandbox output stays attached to the recommendation
  • Every decision has an audit record
  • Policy pages explain privacy, terms, and security posture

Packed with proof-first features

RunProof connects the parts of an incident that usually stay scattered: issue context, logs, runbooks, sandbox output, and the final approval gate.

01Step 1 of 4

Track incidents with evidence

Every alert becomes a structured workspace holding the deploys, logs, metrics, and runbook rules needed to reason about the fix.

  1. Track
  2. Capture
  3. Replay
  4. Gate

Risk analysis

120s

Rollback touches payment-service and its retry queue.

rollbackrisk

Incident tracker

40s

Checkout failure, likely cause, and operator decision in one place.

checkoutp95

Evidence packet

10s

Deploy diffs, logs, and metrics collected before any action.

deploylogs
IntegrationsLive context flow

Designed for the stack that already wakes you up.

Connect the tools that create incident context, then move the work through one visible proof and approval flow.

Source systems

  • GitHub logoGitHubdeploy diffs
  • Datadog logoDatadogmetrics
  • Sentry logoSentryexceptions
  • PagerDuty logoPagerDutyalerts
  • Slack logoSlackapprovals
  • Cloudflare logoCloudflareruntime logs

RunProof

proof layer

Reviewable output

  • Evidence packet

    Deploys, traces, logs, and metrics are grouped for review.

  • Sandbox replay

    Diagnostics run before production is touched.

  • Approval request

    The action stays locked until a person approves.

Evidence first

Ready to review the incident loop?

Create an account, open the console, and walk through an evidence-gated runbook from alert to approval.

Get startedOpen demo console