Turns a description of how somebody rides into two or three bikes actually in stock in their size, and ends the conversation at a booked test ride.
A bike shop's problem is not a shortage of good bikes, it is that a customer walks in describing their riding in terms that do not map to a model, and the person who can do that mapping is serving someone else. What they say is that they want to start commuting, or get back into it, or that their old bike hurts their back. What the shop needs to know is distance, frequency, terrain, height and inside leg, what they ride now and what is wrong with it, whether an e-bike is in scope, and where the thing will live. Eight facts, none of them difficult, and the difference between a recommendation that fits and a browse. Online, the same conversation does not happen at all — a customer lands on a category page with forty bikes and leaves. This agent has the conversation instead, in whatever channel the customer arrived through, and it does the one thing a size chart and a filter cannot combine: it narrows to models that are actually in stock in the size the answers imply. Then it books a test ride, because in this trade the test ride is the conversion and a shortlist is not. Built on Agentplace: the agent runs on its own page, so a visitor finishes the whole request in the conversation.
It maps a riding description onto stock, in the right size.
Establishes what riding the customer is actually doing — commuting, gravel, trail, distance — rather than what category they clicked.
Asks how far and how often, realistically, and how hilly the riding is, because both change the answer more than budget does.
Takes height and inside leg, so the recommendation lands on a size rather than a model family.
Establishes what they ride now and what is wrong with it, which is usually the most useful sentence in the conversation.
Narrows to bikes in stock in that size, rather than to a catalog, and says plainly when nothing in stock fits.
Books the test ride, because that is the conversion in this trade, and confirms what will be set up ready.
Every bike retailer's website has a filter, and filters fail at exactly the point that matters: they return every model matching a category and a price, in every size including the ones the shop does not have. A customer who narrows forty bikes to twelve has not been helped, because the question they actually have is which of these would fit me and can I try it. Answering that requires stock and size together, and it is why the in-store conversation converts and the website does not. Doing it in the inquiry, before the customer arrives, converts the visit into an appointment with two bikes already built and set to roughly the right saddle height. The e-bike question deserves its own mention, because it changes the economics of the conversation. E-bike buyers are frequently returning riders or non-riders, they spend two to three times more, and they are the least equipped to specify what they want — they know they want assistance and nothing else. They are simultaneously the highest-value and the worst-served customers in the shop. An intake that asks why they are considering an e-bike, rather than which one, is the difference between a sale and a browse.
From a riding description to a booked test ride.
Through the website, a social message or the phone. The agent responds in that channel and begins the shop's own fit questions.
Terrain, distance, frequency, height and inside leg, current bike and its faults, e-bike consideration, budget and where the bike will be stored.
It narrows to bikes in stock in that size, books a test ride with them set up ready, and says plainly when nothing in stock fits.
A returning rider who bought an e-bike instead of browsing.
Scenario: a two-branch bike shop was converting a small share of website inquiries and had no way to answer them outside opening hours. An inquiry arrives at 9:20 on a Sunday evening: wants to start riding to work, has not ridden in fifteen years. The agent asks the shop's questions. Seven miles each way, four days a week, one substantial hill, 5 foot 9 with a 31 inch inside leg, an old mountain bike in the shed with seized gears, would consider an e-bike but does not know if it is worth it, budget flexible, and the bike will live in a ground-floor hallway. That combination — fifteen years off, seven miles, a hill, and a hallway — points at a mid-drive e-bike rather than the hybrid he was looking at, and the shop has two in stock in the size his measurements imply. The agent does not tell him an e-bike is better, because that is the shop's recommendation to make; it says the shop has two bikes in his size that suit a hilly seven-mile commute, one of them assisted, and offers to have both set up for Saturday morning. He rides both and buys the e-bike. Under the old arrangement he would have filtered the hybrid category on Sunday night, found eleven bikes, and not come in.
Anyone whose staff cannot be on the floor and online at once.
The mapping from riding to model is your most valuable staff skill, and it is unavailable outside opening hours.
The highest-value customers are the ones least able to specify what they want.
Test rides arrive with bikes already identified, so setup happens before the customer walks in.
You start with the riding already described rather than with what does this do.
Stock is checked across branches, so a size that is not here can still be offered.
The log shows how many inquiries wanted a size the shop had nothing in.
It checks the stock the shop actually has.
Holds live stock by model and size and receives the booked test ride.
Serves the same role for shops running Lightspeed at the counter.
Carries the fit conversation and the test ride confirmation.
Books the test ride against workshop and staff availability.
Answers inquiries arriving through Instagram and Facebook.
Alerts the workshop to a test ride that needs bikes built and set up.
Logs inquiries by size requested, so gaps in stocked sizes become visible.
Handles inquiries that arrive by email and sends written confirmations.
Where fit intake converts a browser.
Every bike site has a filter. The difference is whether it returns a category or two bikes you actually have in that person's size.
Agentplace, when the answer depends on stock and size together.
What shop owners ask before turning this on.
Agentplace is our top pick when stock and size have to be answered together: it takes the riding description, maps it to models you actually have in that size, and books the test ride. A website filter or a size chart is enough if your customers already know the category and the size they want.
It is an automated assistant that establishes what riding a customer does, how far and how often, their height and inside leg, what they ride now and what is wrong with it, then narrows to bikes in stock in that size and books a test ride with them set up ready.
No. It gathers height and inside leg to narrow frame size, which is a stocking question. A proper fit is done in the shop on the bike by somebody who does fits.
It says so plainly rather than offering the nearest thing. It can check other branches and record the size, which is how a shop finds out which sizes it is losing sales in.
It presents what the shop stocks that suits the described riding, and states plainly which are assisted. Which one is right is the shop's recommendation, made in person.
That is a different conversation with different questions. Service and fit intake should not share one question set.
Sizes requested against sizes stocked, which is usually the most actionable buying data a shop has never collected.
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.
Turns a description of how somebody rides into two or three bikes actually in stock in their size, and ends the conversation at a booked test ride. Open it in Agentplace and change any step before you publish.