How Sharely.ai Response works
Follow along as we simulate a payment gateway outage at “ShopFlow” — a mid-size ecommerce platform — and see how the simulator reveals exactly where their incident response breaks down.
Each layer is composable. Change one variable, re-run, measure impact.
Configure the Org Profile
ShopFlow has 4 teams, PagerDuty + Slack for comms, and a policy requiring VP approval before customer notifications. We model all of it as the baseline.
Define the Scenario
A partial payment gateway outage. PayCore's EU endpoint returns 503s, causing 60% of EU checkouts to fail. The US endpoint is fine, making initial diagnosis confusing.
Who responds — and when
Chaos cards enabled
Run the Simulation
50 iterations. Each one uses Claude Haiku to role-play every actor with their full situational context. Every iteration produces a different outcome — because real incidents are stochastic.
Analyze the Results
After 50 iterations, the engine aggregates the data. Here's what ShopFlow's numbers reveal:
TTN Radar (p90)
Minutes until each stakeholder group is notified
Key Metrics
Aggregated across 50 iterations
Where are the minutes going?
Every delay on the critical path is tagged with a cause
Get Ranked Interventions
The engine maps delay causes to concrete fixes, estimates impact from the simulation data, and ranks by expected TTN reduction.
Pre-approve statuspage template for payment incidents
76% of iterations: VP approval added 5-12 min. Pre-approved templates eliminate this.
Add EU-specific error rate dashboard + dedicated alert
40% of iterations: blended metrics masked EU severity. EU-only alerting fires 3-5 min earlier.
Add secondary on-call to Payments (follow-the-sun)
62% of iterations: 2am wake-up added 4-8 min. EU-timezone secondary cuts night latency.
Compare and Iterate
ShopFlow tests intervention #1 (pre-approved statuspage templates). Re-run the simulation. Side-by-side comparison:
Why this works
Incident response is a complex system. Runbook audits and tabletop exercises miss the emergent behavior. Simulation captures it.
Models the real world
- Actors have context: asleep, fatigued, in a meeting
- Tools have mechanics: latency, noise, outages
- Policies have cost: approval chains add minutes
- Chaos happens: injected disruptions fire stochastically
Quantifies the invisible
- TTN is a vector, not a single number
- Every delay is tagged with a cause category
- Critical path shows which delays actually matter
- p50/p90/p95/p99 across N iterations, not one guess
Turns data into action
- Ranked interventions with expected TTN reduction
- One-click re-runs to test each fix
- Side-by-side comparisons prove impact
- Evidence-based proposals, not opinion
Ready to find your bottlenecks?
Configure your org, define a scenario, and get your first TTN analysis in minutes.