What's Involved In Working With An Expert To Build A Chatbot?

Section 7: Taking the Next Step to Your Own ChatbotJuly 4, 2025•7 min read

🏗️ Should You Build Your AI Chatbot with an Expert? Imagine you're opening a new restaurant. You could buy a premade food truck (quick and cheap), or you could hire an architect and chef to build the perfect kitchen and menu from scratch. The second option costs more, but it's tailored to your style, your customers, and your growth.

That's what collaborating with a chatbot expert is like—especially for industries like law, banking, and healthcare, where one-size-fits-all simply doesn't fit.

This article breaks down how working with an expert works—from the first conversation to the live launch. Whether you're running a law firm, a credit union, or a startup focused on customer experience, here's how expert collaboration helps you build the right solution without burning time or trust.

🔍 1. Discovery: Clarify the Why Before You Touch the How

You can't build the right chatbot if you don't know what it's for. In the discovery phase, experts run workshops or interviews with you to understand your goals, like:

Reducing client intake calls by 40%
Automating FAQs about bank accounts
Prequalifying leads for sales

They'll also look at your existing systems (like CRMs or ERPs), your audience, and your infrastructure. For example, a law firm might want a chatbot for client intake—but if client data is stored in an outdated Excel sheet with no API, that'll shape what's feasible.

Discovery Deliverables

🧾 Deliverable: A project brief with goals, user needs, tech requirements, and scope.

📌 Real-world example: MobiDev helped a healthcare company reduce support calls by 15% (saving $5M/year) by starting with a clear discovery process that matched tech to business needs.

📋 2. Planning and Design: Blueprint Before You Build

Once the goals are clear, your expert will define the tech stack (Python? Rasa? Azure?), choose whether to use retrieval-augmented generation (RAG) for dynamic knowledge access, and lay out a roadmap.

Then comes conversation design—mapping how the chatbot will actually talk to users. What if someone types "talk to a human"? What if they ask about refunds in slang? These flows get plotted before a single line of code is written.

Planning Deliverables

🧾 Deliverables: A detailed plan with tech stack, UI mockups, chatbot flows, and a project timeline.

📌 Real-world example: Itransition mapped out customer workflows for a CRM-integrated chatbot that handled 70% of repeat inquiries.

⚙️ 3. Development: This Is Where the Bot Comes to Life

The dev phase includes:

Coding backend logic for things like intent recognition
Frontend UI (website widget? Slack bot?)
Integration with existing tools (like Salesforce or a SQL database)
Optional RAG implementation using tools like Pinecone or LangChain

Security, of course, is part of this—especially for law and banking. Expect features like MFA, TLS encryption, and GDPR compliance.

Development Deliverables

🧾 Deliverables: A working chatbot prototype with real integrations and security baked in.

📌 Real-world example: ValueCoders built a chatbot for a bank that used MFA, integrated with Salesforce, and achieved 95% uptime.

🧪 4. Testing: Try to Break It Before Your Users Do

Testing covers:

Functional QA (Does it understand questions?)
Performance (Can it handle 1,000 queries/hour?)
User acceptance (Do real users find it helpful?)
Security (Does it resist prompt injection or data leakage?)

Experts will use user feedback and edge cases to refine the bot.

Testing Deliverables

🧾 Deliverables: A tested, production-ready chatbot with documentation.

📌 Real-world example: Topflight Apps tested and restructured a sobriety support chatbot that previously failed on open-ended inputs, improving performance dramatically.

🚀 5. Deployment & Ongoing Support: Launch and Keep It Alive

Once tested, the chatbot gets launched—on your site, in WhatsApp, or wherever you want it. But it doesn't stop there.

Post-launch, experts will:

Monitor response accuracy and user satisfaction
Update content as your business changes
Fix bugs and scale as needed
Help with compliance audits (if needed)

Deployment Deliverables

🧾 Deliverables: A live chatbot, monitoring dashboards, and a support plan.

📌 Real-world example: MobiDev maintained a healthcare chatbot that continued to save the client over $5M annually through improved routing and response automation.

⚠️ Challenges You'll Want to Prepare For

Scope creep: Without clear goals, the project can balloon in time and cost.
High initial costs: Custom builds can range from $10K to $100K+. But they're also yours to control.
User resistance: Some people still prefer humans for complex tasks. Smart design and escalation systems are key.
Privacy and compliance: If you're handling sensitive data, experts ensure compliance with GDPR, HIPAA, and more.

Important Consideration

Custom chatbot development can range from $10K to $100K+, but provides control, security, and deep integration that off-the-shelf solutions cannot match.

🧠 TL;DR

Working with an expert to build a custom chatbot means: A structured process from discovery through support, real integration with your tools and workflows, built-in compliance and privacy protections, and a bot that actually fits your business—not a generic plug-in.

Whether you're in banking, legal, healthcare, or retail, expert help ensures your AI chatbot is secure, scalable, and successful.