Booko dynamically prices bookable time slots for service businesses. It fits a studio, med spa, bathhouse, golf simulator, venue or appointment business with high fixed costs and recurring empty slots that cannot be sold after the hour passes.
What it does
Booko’s current homepage connects to an existing booking system, learns historical utilization, availability and demand, predicts which slots will go unsold and surfaces targeted incentives. The customer keeps its booking flow; Booko changes the offer around the at-risk slot. The public examples include fitness, med spas, bathhouses, golf simulators, Pilates, escape rooms and IV therapy.
This is a more specific decision than “use AI for pricing.” The operator has to decide whether filling an otherwise empty slot is worth discounting, credits or membership cashback—and whether the offer can be shown without weakening full-price demand. Booko says discounted slots remain labeled and full-price bookings are untouched; those are company product claims to validate with a real schedule.
The YC profile and launch report early customers seeing around 20% revenue uplift from time that would otherwise go unsold. The homepage also shows example uplift and fill-rate numbers. These are company-reported examples, not an independent pricing experiment. The strongest pilot is one location with a pre-agreed rule for which slots can be discounted.
Founder context and tradeoffs
The YC profile identifies Will Hall and Arjun Saluja as founders. Will has economics and computer-science training and a prior digital-marketing company; Arjun has engineering and computer-science training and built dynamic pricing systems at Joby Aviation’s Uber Elevate team.
Pricing is not public. Booko says the integration can be handled by its team with minimal engineering work. The buyer should ask how offers are approved, how customer segments are selected, how discounts interact with memberships and how the system prevents cannibalizing slots that would have sold at full price.
Editorial take
I would shortlist Booko for a multi-slot service business with a reliable booking history and a clear margin on each appointment. Start with one location and one incentive type. If demand is already consistently full or the business cannot vary prices transparently, dynamic pricing will add complexity without creating capacity.
Quick facts
| Field | Sourced detail |
|---|---|
| Product | Predictive fill-rate and dynamic pricing/incentive layer for bookings |
| Buyers | Fitness, med spa, wellness, venue and appointment businesses |
| Integration | Existing booking systems; Mindbody and Mariana Tek named on homepage |
| Public claim | Around 20% uplift for early customers, company-reported |
| Pricing | Not publicly listed |
Sources checked
| Source | Checked |
|---|---|
| YC profile | 2026-09-19 |
| Booko homepage | 2026-09-19 |
| Booko launch | 2026-09-19 |
Cohort context
Booko is listed in Winter 2026. In our 2026-09-18 directory snapshot, 126 of 199 listed companies in that cohort have YC’s primary industry label B2B (63.3%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19 16:20 UTC in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
