A nail studio’s booking assistant has to do more than keep a conversation going: it needs to capture the right service, preferences, price and requested time in a form the owner can act on. Kane’s prototype for a friend’s studio in GRA, Port Harcourt, uses a LoRA-tuned Qwen3-8B model to collect those details and feed them into a customer queue. Its reported evaluation is promising, but it is a small, builder-run test—not proof that the system is ready for other salons or real payments.
What the booking assistant is meant to solve
The project starts with a practical bottleneck: Sugar, the nail technician, cannot always stop to answer messages or organize walk-ins while working. A customer might message, “Hi dear, are you free today? How much is acrylic?” The front desk is designed to turn that kind of request into a usable booking rather than leave the owner to extract details from a long chat.
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Kane’s stated goal was not simply to make the model sound natural. As Kane put it, “Sugar doesn’t need a chatbot that sounds smart. She needs one that gets the order right: the correct service, the correct price, and the correct time, in the way her customers actually talk, which is often Pidgin.” That focus shapes both the training examples and the application: conversation is the intake layer, while a structured order is what the queue needs.
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Customer booking
A customer opens a link or scans a QR code, then provides a name and phone number, chooses a service and enters preferences such as style, colour, length and shape. The form also asks about allergies and lets the customer request a time. The menu examples in Kane’s account include gel, acrylic, Gel-X, BIAB, French tips, chrome and hand-painted nail art; they are examples from this studio’s context, not a complete menu for other businesses.
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The customer sees an order summary and a total in naira. The described flow then presents payment and issues a queue ticket that updates the customer’s place in line. However, Kane explicitly says, “The payment step is simulated for now.” That means the account describes a payment step in the interface, not a connected gateway, a real charge or a settled transaction.
Queue for Sugar
A second, PIN-protected screen is intended for Sugar on a phone or tablet. It shows the current client and the next clients, along with order details, allergy notes, price and payment status. Sugar can mark a service as started and then done. Kane also says new bookings appear automatically and make a chime sound.
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This design makes the model’s output operationally useful: the customer-facing conversation ends in an order card, and the queue screen gives the owner a place to review and advance that booking. Allergy information is collected and highlighted for the owner, but the account does not establish a formal allergy verification or safety procedure. A highlighted note should be treated as an alert for human attention, not assurance that a service is safe.
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Why fine-tune the model for one studio?
Kane says the system is based on Qwen3-8B, an open-weight model, adapted with LoRA through Tinker. Kane authored about 1,750 booking conversations in Sugar’s voice, using English and Pidgin. Each example ended with a structured order containing the customer’s name and phone number, selected service and preferences, allergy information, requested time and naira total.
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The training objective is therefore narrower than general salon advice. The model is taught to handle the studio’s style of customer conversation and produce the fields its application expects. The application can then check or use the structured record instead of relying on a human to interpret every message from scratch.
Tinker’s official quickstart documents supervised fine-tuning and demonstrates a LoRA training client using base_model="Qwen/Qwen3-8B" with rank 16; its API reference also documents the LoRA client and parameters. This corroborates that the described model identifier and training approach are supported by Tinker’s documentation. It does not independently verify Kane’s particular training run or checkpoint.
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What Kane’s 40-chat evaluation found
Kane reports comparing the base and fine-tuned models on 40 chats that were not included in training. The following figures are the author’s reported results for that evaluation, not independently replicated benchmarks:
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| Measure | Base model | Fine-tuned model |
|---|---|---|
| Fully correct orders | 22.5% (Kane’s reported result on 40 held-out chats) | 95% (Kane’s reported result on the same 40 held-out chats) |
| Correct prices | 35% (Kane’s reported result on 40 held-out chats) | 92.5% (Kane’s reported result on the same 40 held-out chats) |
| Order cards readable by the application | 72.5% (Kane’s reported result on 40 held-out chats) | 100% (Kane’s reported result on the same 40 held-out chats) |
The results suggest that, in Kane’s test, fine-tuning improved the model’s ability to produce complete, correctly priced, machine-readable orders for this studio’s booking context. The account does not publish the prompts, scoring rubric, raw outputs or confidence intervals, so readers cannot assess how correctness was defined or reproduce the comparison from the reported figures alone. Forty chats from one studio’s menu and English/Pidgin context also cannot establish performance for other salons, languages or regions.
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- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Reported training time and per-booking costs
Kane reports that fine-tuning took under five minutes and cost about $1.65. For live use, Kane estimates about ₦0.31 per reply and roughly ₦2.80 for a full booking. These are project-reported point-in-time estimates; the account provides no token counts, billing statement or reproducible cost calculation. They should not be treated as current Tinker rates or a guaranteed cost for another deployment. Tinker’s current model and pricing information can change over time.
What the prototype does—and does not—establish
- It demonstrates a workflow, not just a chat window. The central idea is to turn customer language into a structured service order that can appear in a queue.
- Its reported accuracy is specific to a small test. Kane’s 40-chat results describe this project’s evaluation and do not establish general-purpose booking performance.
- Payment is not shown to be live. The payment step was simulated in the described version.
- Allergy handling is limited to collection and flagging. The account does not describe a complete safety or verification process.
- The target context is narrow. The described system reflects one studio, its menu, and customers communicating in English and Pidgin in Port Harcourt.
That scope is also the prototype’s most useful design lesson: a booking assistant can be judged by whether it captures the details the business needs, not merely by whether its replies sound fluent. The available account supports that as a promising one-studio build, while leaving broader reliability, live payment processing and deployment performance unproven.
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