AI chatbot for a fitness studio chain: From scattered content to real-time answers 

A multi-location fitness studio was losing prospective clients because of unstructured information on their website. Within two weeks, a trained AI assistant was live on the site, answering customers’ questions in real time. Inquiry volume dropped, and customers stopped leaving the site without answers. 

The client operates a network of fitness studios delivering wellness for all levels via high-intensity group classes, personalized training, and recovery zones. From on-demand workouts to progress tracking and community features, they aim to make fitness a lifelong habit.

Business challenge

The multi-location studio had a functional WordPress site and a steady stream of inbound interest,  but the inquiries kept coming in through the contact form and front desk, after hours, when no one was there to answer them. Prospective members wanted direct answers about schedules, pricing, and policies, and the site made them work too hard to find any of it.

• Information spread across separate website pages: class times, membership tiers, cancellation and freeze policies, kids’ program requirements, with no single place a visitor could go to get a clear answer.
• Most inquiries came in evenings and weekends, exactly when someone is on the pricing page deciding whether to sign up, and nobody was available to respond.

The client needed a technology partner with the depth to assess the situation, weigh the available options against the actual business context, and recommend a solution that was viable and pragmatic rather than technically impressive for its own sake.

Solution delivered

Before recommending anything,  GetDevDone scoped and costed two distinct paths: a SaaS integration and a fully custom-built AI assistant, and presented them side by side with timelines, ongoing costs, and honest tradeoffs. The client received the needed data to make an informed call.

Solution rationale

Decision: SaaS solution 

Given the studio’s scale of around 30 pages of content and moderate inquiry volume, the SaaS path was the practical choice. It covered all functional needs without introducing unnecessary complexity. The custom approach would only become relevant if the business expanded into multiple brands or required deeper system control. 

Key solution components & features 

Content audit.  The team audited 30 content pages for accuracy before AI training. By correcting conflicting schedules, pricing, and outdated policies, we prevented the system from scaling human errors. The result: an AI that retrieves facts instead of contradictions. 

Content preparation. GetDevDone’s engineers went beyond uploading pages and built the knowledge base around real customer demand, structuring the 15 most frequent questions from the studio’s contact form submissions and front desk logs into dedicated Q&A pairs, organized by topic. The assistant responds from the right context, instead of pulling fragmented answers.

Defined scope & guardrails. The assistant’s tone was set to match the studio’s brand voice, and its boundaries were defined just as deliberately. The AI is configured to handle schedules, pricing, and policies, while automatically deferring out-of-scope or high-risk tasks, like fitness advice or payment processing, to the human team via contact form. The studio’s voice stays consistent, and the workflows that matter stay protected.  

Performance validation. Before launch, the QA team pressure-tested the assistant against 30+ real past inquiries to ensure production-level accuracy. After identifying and closing gaps in location-specific and program information, the system achieved a high-confidence response rate. We implemented a ‘human-in-the-loop’ protocol for complex edge cases, so no customer inquiry falls through the cracks in the future.

Widget deployment. The chat interface is integrated directly into the client’s WordPress site to capture visitors at high-intent touchpoints like pricing and schedule pages. To respect the user journey, we optimized the widget for mobile performance and set a delayed launch, so that the assistant provides support only after the customer has had the opportunity to explore the primary site content. 

Technologies & tools 

SaaS (selected)

• ChatBot by LiveChat: conversational AI platform; hosting and AI processing 
• WordPress widget integration: chat interface  

Custom path (scoped)

• Flowise: LLM orchestration
• OpenAI GPT-4o-mini: question answering
• pgvector / Supabase: semantic content retrieval
• Node.js: back-end runtime
• Firebase Cloud Functions: automated content sync

 Business outcomes

Within two weeks of kickoff, the studio received a trained AI assistant handling real customer questions on the live site. The rollout moved directly from setup to practical use, without any rework or transition period.

Reduced pressure on front desk and contact channels
Schedules, pricing, and policy questions are resolved inside the chat. Monthly inquiry volume shows a measurable drop in repeat requests, giving the team capacity for in-studio operations and higher-value interactions.

Answers timed to the customer’s decision window 

Visitors exploring options after hours get immediate responses while they’re still considering a booking or membership; the information reaches them in the moment it matters.

Content stays current through the studio team
All the critical information updates are managed directly in the ChatBot dashboard by the studio’s admin. Changes reflect in responses as soon as the content is saved, keeping it accurate without additional development steps.

Brand voice and scope consistency 

The assistant operates within explicitly defined boundaries, handling in-scope inquiries about schedules, pricing, and policies, and routing everything outside that scope to the contact form. The studio’s brand voice and customer experience stay safe across every interaction.