Sharely.ai Response
Incident Response Intelligence

Simulate incidents. Measure TTN. Fix before they break.

Model the full complexity of your incident response — signal detection, human decisions, communication tools, org policies, and emergent chaos. Get ranked interventions to reduce Time to Notify before your next real incident.

Everything that shapes your incident response, modeled

Seven interacting subsystems simulate how incidents really unfold in your organization — from the first alert to the last stakeholder notification.

Signal Detection

Race multiple signal sources — threshold alerts, anomaly detection, SLO burn rates, customer tickets. See which detection strategy fires first and with what confidence.

Actor Simulation

AI-driven actors role-play your responders with realistic situational context — on-call fatigue, meeting conflicts, VPN issues, and experience levels.

Comms Delivery

Model real tool mechanics — PagerDuty ack timeouts, Slack noise levels, escalation policies, bridge creation delays, and tool outage correlations.

Chaos Injection

Toggle injectable chaos cards — dashboard outages, conflicting runbooks, vendor status page inaccuracies, exec escalations mid-triage.

Dependency Graphs

Service ownership, hard/soft dependencies, failure propagation, and ownership ambiguity that causes misrouting delays.

Policy Modeling

Severity declaration rules, notification thresholds, approval chains, statuspage policies, and paging restrictions — all configurable.

Three layers, one clear answer

Composable configuration means apples-to-apples comparisons. Change one knob, re-run, measure impact.

1

Org Profile

Configure your baseline: teams, roles, comm tools, policies, service dependencies, signal sources, and resource constraints.

2

Scenario

Define an incident: narrative, severity, affected services, actors with roles, active signals, and chaos cards.

3

Run + Analyze

Execute N iterations with Claude Haiku actors. Get TTN distributions, delay attribution, critical paths, and ranked interventions.

TTN is a vector, not a number

Every notification target has its own timeline. Every delay has a cause. Every cause maps to an intervention.

TTN Vector
Per-target notification timing
11
Delay cause categories
Critical Path
Causal chain analysis
Ranked
Intervention recommendations

Stop guessing. Start simulating.

Know your TTN p90 before leadership asks. Test interventions before deploying them. Show the data, not the spreadsheet.

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