01
26-hour quote lag
Event planners shop 3-5 agencies. First complete quote wins the booking. By Friday morning the agency was already losing the weekend to competitors who quoted Thursday night.
All work
Quote response: 26 hours → 9 minutes.
A regional event staffing agency was losing weekend bookings because event planners were getting quotes from competitors first. We built a quote-to-roster engine that parses inbound event requests, generates draft quotes in minutes, and stages a day-of comms layer that all but eliminated the Saturday-night dispatcher fires.
Great brand, great workers. Losing deals because they couldn't quote fast enough.
Event staffing is a different shape from healthcare or industrial staffing. Events are 4-8 hour engagements, demand is brutally spiky (Friday-Sunday is 70% of bookings), and a single no-show isn't a fill-rate stat. It's a brand-damaging incident in front of the client's biggest moment of the year.
The agency placed bartenders, servers, brand ambassadors, security, registration staff, and event captains across ~120 events per month, with a roster of ~1,200 active gig workers. Three structural problems were squeezing margin and dispatcher sanity at the same time.
02
Saturday-night fires
Last-minute cancellations triggered manual phone trees and group texts. Three of five dispatchers were burning out on a 12-18 month cycle. Replacing them was a permanent line-item.
03
Day-of comms chaos
Every worker needed a tailored briefing. Dispatchers sent them manually, in bulk, sometimes with errors, surfacing as panicked venue calls the morning of the event.
We had a great brand and great workers. We were losing deals because we couldn't quote fast enough on Friday afternoons, and our dispatchers were quitting because Saturday nights were unsurvivable.
Quote generation first. Most measurable revenue. Lowest risk.
LAYER 1 · SHIPPED FIRST
Inbound quote generation
Parse the event request, classify it, look up similar past bookings, draft a complete quote: staff counts by role, current rate cards, total estimate. Human approves and sends. Target: minutes, not hours. Most measurable revenue impact, lowest technical risk.
LAYER 2 · BUILT NEXT
Event-aware roster building
Hard filters first (certifications, geography, availability), then a Claude-powered scoring layer: event history, venue history, client history, prior ratings. Output: a ranked, dispatcher-ready roster. The dispatcher reviews, swaps if needed, and approves.
LAYER 3 · CLOSED THE LOOP
Day-of comms orchestrator
Personalized briefings auto-sent the day before and morning of: venue, parking, call time, dress code, supervisor contact, role-specific notes. GPS check-in confirms arrival. High-stakes events get a standby worker pre-warmed within 30 minutes if needed.
We built Layer 1 first because the revenue signal was direct and measurable: every hour shaved from quote response time was a calculable share of the Friday-Sunday booking window. Validate the lift, then stack Layers 2 and 3 on a proven foundation.
Quote engine → Roster builder → Day-of orchestrator.
Three systems that hand off cleanly. A planner's inquiry enters the top; a fully briefed, GPS-checked worker arrives at the venue exit.
From firefighting 12 hours to running the high-risk desk for 4.
The dispatcher team didn't shrink. Their job changed. Two of the five who were closest to burning out moved to a "high-risk event desk" handling only the events the system flagged as needing white-glove attention. The other three shifted from reactive firefighting on Saturday night to proactive client communication and worker quality coaching during the week.
The revenue story is the win rate.
26h→9m
Quote response time (avg)
+18pts
Booking win rate (23% → 41%)
−7pts
No-show rate (11% → 4%)
~60%
YoY revenue growth post-launch
Six numbers. Every one moved in the right direction.
| Metric | Before | After |
|---|---|---|
| Quote response time (avg) | 26 hours | 9 minutes |
| Quote → booking win rate | 23% | 41% |
| Day-of fill rate (at event start) | 84% | 97% |
| Worker no-show rate | 11% | 4% |
| Dispatcher Saturday-night hours | 12-14 hrs | 4-6 hrs |
| Events handled per dispatcher / week | 18 | 32 |
Both were spreadsheets in a trench coat. They actually shipped what their proposal said they'd ship, and on Saturday night, the system was making decisions my dispatchers used to make at 2am.
Diana P. · Operations Director
Built on your stack. Integrations, not replacements.
- Models
- GPT-4 (quote-request parsing, briefing generation), Claude (worker matching & scoring rationale)
- Outreach
- Twilio (SMS), SendGrid (email)
- CRM / event management
- Existing platform: built integrations, didn't replace
- Worker app
- Kept existing app; added endpoints for confirmation, briefing receipt, GPS check-in
- Database
- Postgres + pgvector for past-event similarity lookup
- Infrastructure
- AWS, deployed in agency's environment
10 wks
Timeline · 3-wk prototype + 7-wk build
5
Team · AI, engineers, delivery, designer
$72K
Budget cap · billed weekly
~$69K
Actual spend · ~4% under cap
30d
Post-launch tuning window included
Sound familiar?
Book a workflow audit
If your dispatchers are firefighting Saturday nights, that's the workflow. Tell us where the fires are. We'll tell you whether this engine fits your operation.
No deck · No demo · No sales pressure