Booko gives every service business the revenue superpowers that, until now, only the largest companies in the world could afford.

Every hour that goes unsold converts to zero. Every hour priced wrong leaves money on the table.

This is true of basically anything you can book.

Uber, airlines and hotels figured this out a long time ago. They solved it by throwing an enormous amount of intelligence at the problem.

They have teams of ML engineers, pricing scientists, and revenue managers whose entire job is figuring out demand and price:

• What will sell, what won’t, and why?
• What will customers pay, and what should it cost?
• Who should we incentivize, and when?
• How much supply should we have, and how do we make the most profit across everything we sell?

They have a hundred math PhDs sitting around the table figuring this out.

Now look at the rest of the service economy.

Tours. Fitness studios. Med spas. Salons. Clinics. Tickets. Experiences. Anything you can book.

Every service business has the same fundamental problem. They just haven’t had the hundred math PhDs.

Every business is different. Their data lives across booking systems, CRMs, data warehouses, and marketing platforms.

And a hundred-location business isn’t one pricing problem - it’s a hundred interconnected ones.

Changing the price of one slot changes how customers behave across the slots around it. Raise the 6pm slot and some customers pay it, some move to the 7pm, some go to a competitor, and some don’t come at all. Discount the 7am and you might bring in new customers, or just give a discount to people who would have paid full price at 8. Change one price and the effect ripples across the whole schedule, so every slot has to be priced with all the others in mind.

There are patterns to learn across the network, but every location has its own customers, its own rhythms, and its own set of factors that determine demand.

So you can’t just take a generic pricing algorithm and turn it on.

And it all needs to work natively, correctly, and without tarnishing the brand.

Historically, that was an end-to-end, forward-deployed consultancy project for every business.

So, as a service business, you had two options:

1. Build an expensive ML and revenue team yourself.
2. Pay McKinsey a few million dollars.

Neither makes sense. Even very large, multi-location operators can have only a handful of engineers, already barely holding up their own infrastructure.

The market stayed unserved because nobody could afford to serve it.

AI changes the economics

Dynamic pricing isn’t new. Doing it well for every individual business and location was too bespoke and too expensive to scale, until AI collapsed that cost.

What once required teams of ML engineers, data scientists, consultants, and revenue managers can increasingly be done by agents.

The hundred math PhDs, now at your fingertips.

AI is changing who buys, too

Personal agents are here, and online commerce is just starting to feel it. Soon, consumers won’t need to tediously browse booking sites. They’ll tell an agent that knows their schedule, preferences and budget what they want, and the agent will do the searching. Unlike people, agents don’t get tired and don’t settle. They find the best option for their shopper faster than any person could.

We expect a few new behaviors to emerge, and we’re already seeing some of them. Left unchecked, each one costs a business revenue:

• Snipers: when a slot is in high demand, the fastest agent gets it the moment it opens. A slot that sells out in seconds was priced below what people would have paid, and the difference goes to the fastest agent or to whoever resells the slot.
• Deal finders: the agent books, keeps checking the price, and cancels and rebooks the moment it drops. The normal strategy of late price cuts is no longer viable, as the discount goes to people who would have paid full price.
• Price limits: a person barely notices the gap between $49 and $51. An agent told "under $50" books one and never considers the other. Instead of tapering off as price rises, demand falls sharply at the limit.
• Channel routing: for a given slot, the agent finds the cheapest price, regardless of where it lives. Any price gap between booking channels gets found, and bookings flow to whichever one undercuts the rest. Agents will avoid marketplace premiums and book direct instead. Thus, optimizing the price that the business offers becomes crucial.
• Hoggers: the agent books every slot it might want while cancellation is free and cancels the rest right before the deadline. To the business, the slot looked sold, then was refunded too late to resell. We expect cancellation policies to adapt to mitigate this.

These behaviors are a clear win for the consumer. Businesses need something to level the playing field.

We believe dynamic pricing, done well, fills that need for service businesses. Agents push every market toward efficiency, and in a perfectly efficient market, a mispriced slot, in either direction, leaves revenue on the table. A business pricing as if people are still doing the booking will be outmatched.

In practice, that means every slot is priced at what it’s worth before an agent gets to it: hot slots stop selling out in seconds, soft slots don’t need late price cuts for agents to chase, and prices land on the right side of the limits agents carry. Paired with cancellation rules built for agents, dynamic pricing levels the field. That’s what Booko builds for every service business.

Booko builds the brain, and does the work

Booko embeds into the systems a business already runs on and builds a superintelligent brain around its demand.

Our agents look back through years of demand data to understand what happened and why. Maybe it was a blizzard, a local event, a holiday, a competitor changing its prices, or a great marketing campaign. Booko identifies which of those things actually moved demand, learns from those relationships, and uses them to make better decisions.

But history only shows what happened at the flat prices the business charged. It cannot say how well the Thursday 6pm would have done with a $5 incentive. So Booko tests the market, carefully.

The goal is to learn what every slot is actually worth and how its customers respond to price, without putting the business’s revenue at risk to find out.

From every answer, Booko learns two things about each slot: how much a price change moves people, and whether those people move to other slots or leave. It leans on the prices that are working and keeps testing the ones it’s unsure of, so each lesson costs as little as possible. Every week, the business charges closer to what each hour is actually worth.

Booko becomes the world expert on demand and price for that business:

What is going to happen? Why is it happening? What will people pay? And what should we do about it?

Then Booko deploys that intelligence across the business

The first place that intelligence lands is price.

Booko prices the whole schedule as one system. If a slot is likely to go unsold, Booko prices or incentivizes it inside the systems the business already uses. If a slot is priced below what it’s worth, Booko finds that too. The goal is the most profit across everything the business sells. Filling one class by emptying the next one doesn’t count.

The same forecast can tell us who is most likely to respond to an incentive, when to reach them, and what to tell them.

That’s targeted marketing tied directly to demand. The customer list no longer sits in static segments built in Excel six months ago.

The same intelligence extends to staffing, scheduling, inventory, and promotions, which all sit downstream of demand. Today, those decisions live across different teams and different systems.

Booko becomes one brain across the business.

It watches demand, learns what people will pay, figures out what the business should do next, and does it.

For the operator, Booko runs quietly in the background like a revenue team they never had. They set it and forget it while Booko works to make them more money.