spike-proof
Dedup before tickets open
Repeat reports of one outage collapse into a single incident with all sources linked.
// industries / e-commerce
FlowTux for e-commerce teams
E-commerce support has a shape problem, not a volume problem. You staff for the average and then a checkout failure on the busiest day of the year produces forty reports of one incident in ten minutes, while genuinely different issues queue behind them.
FlowTux is built for that shape: duplicates collapse before they become work, priority tracks revenue impact rather than arrival order, and the routine repeat questions resolve without a human.
The peak-day problem is duplication, not demand
Forty reports of one outage is one incident and thirty-nine interruptions.
During a spike, the same failure gets reported by everyone who hits it — in Slack, by email, through the store’s own channels. Handled naively, each report becomes a ticket, and the team spends the incident triaging copies while the actual fix waits.
Semantic deduplication collapses them into one incident with a count and every source thread linked, so the team works the problem once and the update broadcasts everywhere it was reported.
Priority that tracks money, not arrival order
Checkout down outranks a catalogue image, whatever the queue order says.
A first-in-first-out queue treats a broken checkout and a misaligned product image as equals until a human intervenes. Triage that reads impact sets severity from what is actually affected — payment path, cart, catalogue, cosmetic — so the revenue-critical failures rise immediately.
The same logic gates paging: checkout and payment failures page; a cosmetic issue becomes a normal queue item, which is what keeps the pager credible at 2 a.m. on peak weekend.
Scale without seasonal hiring
The repeat questions that dominate peak volume are the ones AI closes end to end.
Peak volume is disproportionately repetitive: the same handful of questions and known issues, asked by more people. That is exactly the profile that auto-resolves well inside an allow-list, with every action logged on the ticket.
The result is a team that handles the spike by handling the genuinely novel share of it, rather than by hiring temporary staff to answer the same question four hundred times.
What you get
Revenue-weighted priority
Severity set from what is affected — payment path, cart, catalogue, cosmetic.
gated
Paging that stays credible
Only revenue-critical failures page; the rest queue at normal priority.
Auto-resolution for repeats
The repetitive majority of peak volume closes inside an allow-list, with an audit trail.
One queue across channels
Slack, email, and WhatsApp reports deduplicate against each other.
$49/mo flat
No seasonal seat math
Flat pricing means bringing in extra hands for peak does not change the bill.
Get started for e-commerce
- 1
Connect your intake channels
Slack, email, and WhatsApp into one queue.
- 2
Define severity by impact
Map payment, cart, and catalogue failures to your severity levels.
- 3
Verify dedup on a real spike
Check the collapse ratio before peak season, not during it.
- 4
Allow-list the repeats
Promote the categories that recur every peak to auto-resolution.
Related
Frequently asked questions
How does FlowTux handle peak-season ticket spikes?
Primarily through deduplication: repeat reports of the same outage collapse into one incident with an occurrence count and every source thread linked, so the team works the problem once. Routine repeat questions — the bulk of spike volume — auto-resolve inside an allow-list.
Can priority reflect revenue impact?
Yes. Severity is set from what is actually affected rather than arrival order, so a checkout or payment failure outranks a cosmetic catalogue issue immediately, and only revenue-critical failures page.
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