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Munemo — our own AI product for sales teams. Staff just speak or type after a call; AI fills the customer summary, tracks what changed, and turns it into next actions (Jira ticket, email, Slack, reminders).
自社プロダクト「Munemo」。商談後に話すだけで、AIが顧客サマリーを自動入力し、変更点を記録。次のアクションをJira・メール・Slackへつなげます。
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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I’ve always liked products that solve a very specific problem instead of trying to do everything.
So for the Lovable Challenge, I picked a simple question that event-tent rental businesses hear all the time:
“Can I book a tent for my wedding?”
The problem is… a tent can’t really be booked until you know if the whole event will work.
How many guests?
How much usable space?
Tables? Dance floor? Bar? Stage?
Will the layout actually fit?
Can the crew set it up and pick it up on time?
Usually, the owner has to figure all of that out manually through messages, calls and calendar checking.
So I built TentFit — a spatial booking experience for event tent rentals.
The customer starts by planning the event, then maps out the space and everything they want inside it. TentFit checks the layout and highlights conflicts.
And instead of just saying “doesn’t fit,” it helps solve the problem:
Switch the tent. Move the layout. Remove an element.
Once everything works, TentFit checks the logistics and turns the final setup into a booking.
The goal was simple:
Make the customer’s decision easier, and make the owner’s job easier.
The flow is basically:
FIT → FEASIBLE → BOOKED
I had a lot of fun building the spatial planning part of this one.
X/Twitter post: https://x.com/brjanov/status/2105630218492731501?s=20
Built with @Lovable
#LovableChallenge #ProductDesign #UXDesign #NoCode #SaaS #WebDesign
Nice stuff!
Rebuilding the moment an inbound inquiry turns into a confirmed booking for MEND Appliance Care, a local repair shop in Spalding, Lincolnshire.
The Problem Solved: Replaced 2.5 hours of daily owner phone-tag, vague fault descriptions, wrong van parts, and customer no-shows with an instant AI-powered diagnostic intake, fixed upfront pricing, deposit collection, and auto-generated dispatch scheduling.
Key Features Built in Lovable:
• Multi-Modal Intake: Customers describe the issue using text, photos, model numbers, or video.
• Instant AI Diagnosis & Transparent Pricing: Autodetects fault & exact part (e.g., Bosch Serie 6 drum shock absorbers) with clear labor/part cost breakdowns.
• Flexible Resolution Paths: Home Repair, Workshop Drop-Off, DIY Part Purchase, or local stockist routing (Toolstation/Screwfix).
• Owner Safety Net & Dispatch Dashboard: Real-time job calendar, "Ask Maya" human-in-the-loop review queue, and 1-click customer comms.
Built entirely with @Lovable for the #lovablechallenge.
Shop owner dashboard : Email: maya@mend-demo.co.uk
Password: MendDemo2026
The "Ask Maya" human-in-the-loop queue is one of those details that separates a clever demo from something you can actually ship to a real business. Most AI intake flows skip this — they either automate everything and break on edge cases, or hand off to a human and defeat the...
That works. If the screen recording clearly demonstrates the product, your written explanation can carry the “what I built and why” story.
Here’s a polished version you can use for your Contra challenge submission:
What I built & why
I built PawRoute, a smart booking and automation experience for a fictional solo mobile dog groomer, Maya, based in Austin, Texas.
I chose mobile dog grooming because booking isn't as simple as finding an empty calendar slot. Every new inquiry can require the owner to check the customer's location, understand the dog’s needs, recommend a service, calculate pricing, find a time that works with the driving route, confirm the appointment, send reminders, and handle cancellations.
I wanted to answer one question:
How can I turn “Can I book?” into “You’re booked” with as little work from the business owner as possible?
So I designed PawRoute around a simple 3-step customer booking experience. Customers provide their location and dog details, receive a recommended service with clear pricing, and choose from available appointment times.
I also created Best Route Match, which prioritizes appointments when Maya is already working nearby instead of treating every available time equally.
Behind the customer experience, PawRoute handles the repetitive work: booking confirmations, reminders, customer information, route updates, rescheduling, and automation activity.
I also designed an owner dashboard where Maya can see appointments, customers, her driving schedule, and what PawRoute has automated.
Finally, I added a cancellation-recovery workflow. When an appointment opens up, PawRoute can help identify suitable waitlisted customers and refill the slot instead of requiring Maya to manually message people.
The goal wasn't simply to build another booking website. It was to design a system where the customer sees simplicity while the complexity is handled behind the scenes.
3 steps for customers. Less scheduling work for Maya.
Built with Lovable for the Lovable Challenge.
