Answer "which e-bike for a hilly fifteen-mile commute?" with two specific models, a range estimate and a test ride — instead of a category page.
A rider does not want a list of e-bikes. They want to know whether a 500Wh battery survives a fifteen-mile commute with three hundred meters of climbing, whether the bike fits down their basement stairs, and whether a mid-drive is worth the difference. This agent asks the six things that decide it — distance, terrain, load, storage, budget, rider size — and answers with specific models from your floor. Built on Agentplace: the agent runs on its own page, so a visitor finishes the whole request in the conversation.
Turns a use case into a shortlist.
Establishes commute distance and elevation, then estimates real range rather than quoting the manufacturer number.
Asks about cargo — child seat, panniers, groceries — because it changes the frame and the motor class.
Establishes storage: stairs, hallway, shed or garage, which rules out weight and folding decisions.
Separates hub-drive and mid-drive on the basis of the rider’s hills, not on margin.
Applies legal class limits for the rider’s location, so nothing illegal to ride is recommended.
Returns two or three specific models in stock, in the right frame size, with a test ride offered.
Bike shops win on advice. Online, that advice is replaced by a sidebar of checkboxes that cannot reason about a fifteen-mile commute with hills. A conversation that reaches a specific model and a test ride converts far better than a category page, and it is exactly what the shop floor already does well.
Use case, constraints, shortlist.
The agent takes distance, elevation, frequency and surface — a flat five-mile towpath and a hilly fifteen-mile road commute need different bikes and different batteries.
It asks about cargo, storage, stairs, rider height and inseam, budget, and the legal class allowed where they ride.
It proposes two or three in-stock models in the right frame size, explains what separates them, and books a test ride on the strongest fit.
Fifteen miles, three hundred meters of climbing, basement stairs.
Scenario: A rider messages on a Sunday: fifteen miles each way, four days a week, a long climb near home, a bike that has to go down basement stairs, budget up to 3,200 dollars. The agent estimates that thirty miles daily with sustained climbing will draw down a 500Wh pack, and recommends 625Wh or charging at the office. It rules out the 27-kilogram cargo model on the stairs and proposes two mid-drives at 22 and 24 kilograms — one with a rack already fitted, one 400 dollars cheaper without. It confirms a 56cm frame from the rider’s height and inseam, and books a Tuesday evening test ride on both. The shop opens Monday to a booked ride and a rider who already knows why mid-drive costs more.
Shops that sell on advice, not on price.
E-bikes carry the highest ticket and the highest return rate when mis-specified.
Range and motor choice are the entire conversation and the entire risk.
Stock and frame sizes differ by store and the shortlist has to know.
Answering the same six questions all day is not selling.
Load, child seats and storage constrain the choice more than budget does.
Voucher limits and scheme rules narrow what can be recommended.
Stock, calendar and the rider’s thread.
Reads live stock and frame sizes so nothing is proposed that is not on the floor.
Matches models and sizes against point-of-sale inventory across stores.
Books the test ride into the workshop or sales diary.
Confirms the test ride by SMS and reminds the day before.
Sends the shortlist with the reasoning from your own address.
Follows up riders who got a shortlist but did not book a ride.
Posts tomorrow’s test rides and the models to prepare to the shop channel.
Records the use case and shortlist so a salesperson can pick up the thread.
Where a conversation beats a filter menu.
E-bikes carry the highest ticket and the highest cost of a mis-sale. Where e-bikes are a real part of the floor, the recommendation agent is the one that pays for itself first.
Use the AI E-Bike Recommendation Agent when riders cannot self-serve the range question
Range, legal classes and what the agent will not claim.
Run the e-bike agent if e-bikes are the high-value, high-confusion part of your floor. Run the fit and model intake agent if the shop sells across categories and the recurring problem is sizing and general model choice.
An AI agent for e-bike recommendations establishes a rider’s commute distance, elevation, cargo needs, storage, budget and body measurements, estimates realistic range for that use rather than quoting a manufacturer figure, and returns two or three in-stock models in the right frame size with a test ride offered.
From your own data — assist level, rider weight, elevation and terrain — rather than the manufacturer’s best-case number. Overstating range is the fastest route to a return, so the agent is set up to be conservative.
It applies the class rules you configure for the regions you sell into — speed limits, throttle rules, age restrictions and where each class may be ridden — and will not recommend a bike the rider cannot legally use.
It gets to a frame size from height, inseam and riding style, and says plainly that the final fit happens in the shop. Sizing is a starting point online and a fitting in person.
It says so and offers what is — including lead times on order, and the nearest store holding it, where you sell across locations. Proposing a bike that does not exist is how a test ride becomes a complaint.
No. It ranks on fit to the stated use. Recommending a mid-drive to someone with no hills produces a return, and returns cost far more than the margin difference.
It runs on Agentplace. Agentplace is an AI agent platform where the agent gets its own page, talks to your visitors there, and carries the request through to the end instead of handing it to a form. You can open this template and change any step before you publish it.
Answer "which e-bike for a hilly fifteen-mile commute?" with two specific models, a range estimate and a test ride — instead of a category page. Open it in Agentplace and change any step before you publish.