MassageBook
Building a complex, context-aware AI booking bot.
Published 2024
Services
Product Architecture
Product Design & Prototyping
Front-end Development
Back-end Development
AI Integration
Timeline
4 months
About MassageBook
MassageBook is one of the largest massage therapy marketplaces in the U.S., used by tens of thousands of independent therapists and wellness studios. The platform makes it easy for people to find local massage services and book them online. But as the company scaled, so did the complexity of user interactions — especially on mobile, where people expect fast, frictionless booking without digging through filters or drop-downs.
The Problem
MassageBook approached us with a vision:
Create an AI-powered booking assistant that could handle full conversations with users — helping them book, cancel, or reschedule appointments in natural language.
But this wasn’t just a generic chatbot request. The assistant needed to:
Understand vague or partial inputs from users (like "my back hurts" or "what’s available after work?")
Offer real-time availability across 30,000+ businesses
Recommend relevant services based on intent
Distinguish between similar words used for locations and business names
Authenticate users securely and initiate payment
And it had to run in three environments:
On business websites (for individual providers)
Inside MassageBook's native apps
On MassageBook.com for marketplace-wide queries
What made this project complex
The real challenge and where the value was created came from solving problems in three tough areas: real-time logic, ambiguity in user intent, and large-scale system integration.
Our Approach
1. Conversational flow with logic behind every step
We started by designing a flow that worked across five core booking scenarios (see visual reference).
Most users didn’t come in knowing all the details. So the bot had to:
Prompt for location first (always the anchor point)
Ask if they had a specific provider in mind or wanted suggestions
Then clarify service type, date, time, and staff
The system had to be able to move flexibly in this flow, not just ask the same questions in order depending on how the user phrased things.
For example:
“I want a massage near downtown Chicago on Tuesday around 3” → Bot would infer location, date, time
“When’s Sarah G. next free?” → Bot would isolate staff name and cross-reference against Sarah’s schedule
This meant tracking variables in-session across a conversation and dynamically narrowing options without re-asking.
2. Understanding messy input in real time
People don't use clean, category-aligned language. One of the core technical problems we solved was interpreting vague requests like:
“Back massage”
“Pain relief”
“Relaxation therapy”
“Not too deep but not soft either”
MassageBook had hundreds of actual service categories. For example, “Deep Tissue,” “Sports Massage,” “Trigger Point Therapy,” and “Myofascial Release” could all potentially fit what a user meant by “back massage.”
We built a custom matching algorithm trained on:
Category tags provided by MassageBook
Historical user queries and booking behaviour
Linguistic patterns (e.g. what users mean by “deep,” “tension,” “injury”)
This system would interpret input, find the closest match(es), and suggest or auto-select options depending on confidence level. If uncertain, the bot would politely ask a clarifying question or offer choices.
3. Real-time availability search at scale
This was probably the hardest technical piece.
We had to access and filter real-time availability data for over 30,000 businesses. Availability wasn’t just binary, it depended on:
Location
Provider schedules
Duration of services
Existing bookings
Type of service
MassageBook provided a set of APIs, but we still needed to build a middle layer that:
Made requests intelligently (with as few calls as possible)
Parsed the data for gaps and overlaps
Prioritised results based on what the user asked for (e.g. “after 5pm” meant anything between 5–9pm)
Dealing with missing or incomplete data
We implemented smart caching, request batching, and a queueing system to avoid rate limits and reduce latency. So when a user asked “What do you have this Thursday evening?” the bot could respond within a second or two — not ten seconds, not minutes.
4. Smart Suggestion & Ranking Logic
One of the most powerful parts of this system is how it chooses what to suggest. It doesn’t just sort by distance or time. The bot looks at several signals like provider rating, how often that service is booked, past user preferences, even cancellation rates to recommend options that feel high quality, not just “close by.” So if there’s a better option slightly further away, it gets bumped up in the results. That way, the user always gets the best fit, not just the fastest.
5. Authentication and payments
To make sure returning users could easily view or manage bookings, we set up a lightweight but secure authentication system using Twilio for SMS-based login. If the user already had a profile, the bot could then pull up past appointments and stored payment methods.
We added fallback login via password (for users who didn’t want SMS) and integrated the final step payment by redirecting to MassageBook’s existing payment gateway.
This created a seamless flow from question to confirmation in one uninterrupted conversation.
Results and Impact
The AI Booking Bot is now fully integrated across the MassageBook platform — available on individual business websites, within their mobile and desktop apps, and directly on the MassageBook.com marketplace. Since launch, it’s delivered meaningful improvements across the board:
30% reduction in customer support costs
The bot can handle the bulk of booking, rescheduling, and cancellation interactions — significantly reducing the need for manual support.
Checkout time cut from 7 minutes to under 1 minute
What used to be a slow, multi-step process is now completed in a conversational flow, reducing friction and improving the overall user experience.
Higher conversion rates across booking flows
By offering smarter matches and fewer drop-offs, the system can convert more visitors into completed bookings.
Improved vendor reach and engagement
The bot’s smart matching engine connects users with the best available providers even outside their immediate search radius, increasing visibility for vendors and improving outcomes for users.
These benefits will translate into real business value:
Lower operational overhead
Faster and more intuitive booking journeys
More revenue captured through better conversion
Happier users who don’t need to fight the interface to get what they need
This isn’t just a chatbot, it’s a deeply integrated AI assistant that understands how people talk, what they want, and how to turn that into action across a complex service marketplace.
Timeline
This project was delivered over the course of four months, covering everything from early ideation and prototyping to rigorous testing and full deployment.
Tech Specs
Frontend
Next.js – App directory support and server-side rendering
React – For building a responsive, component-based UI
Tailwind CSS – Utility-first styling with custom animations
React Hook Form – For handling conversational input and validation
Lucide + React Icons – Lightweight icon rendering
Backend & AI Integration
Custom-built agent runner – Managed through
vite-node, powering the chatbot logic
LangChain – Used selectively to handle more abstract user queries and prompt composition
OpenAI API (via LangChain) – Used selectively to handle complex or vague user phrasing, fallback prompts, and natural language understanding. The bot relies on a hybrid approach: LLMs for linguistic nuance and custom-built logic for structured booking flows.
tRPC – For fully typesafe API communication between frontend and backend
Express – Lightweight server for API middleware and external integration
Prisma – For database access and model handling (generated on build)
Fuse.js – Lightweight fuzzy search used in service and location disambiguation
Data layer & logic
Custom matching algorithm – Trained to classify vague input (like “back pain”) into relevant categories
Ranking engine – Takes into account factors like provider rating, popularity, and distance to suggest optimal options (not just nearest ones)
Real-time availability querying – Aggregates and filters data across 30k+ vendors
Smart fallback handling – Ensures the bot keeps going, even if some data sources fail
Infrastructure
Twilio – For secure SMS-based authentication
SuperJSON – For serialising data across frontend/backend calls
Zod – For runtime type validation
Environment validation layer – Ensures safe deployment across staging/production
Dev Experience & Tooling
TypeScript – End-to-end typing for safety and dev speed
ESLint + Prettier + Husky – Code quality enforcement and pre-commit checks
Tailwind Merge – For clean and efficient class handling
PostCSS – For extended CSS features and builds
