How I Built an AI Chatbot to Recommend a CPQ Platform (For Learning Purposes Only)
By Reza
Artificial intelligence is transforming how businesses evaluate software solutions—but the best way to understand its potential is to build something hands-on. Recently, I created a simple AI chatbot capable of recommending Configure–Price–Quote (CPQ) platforms based on a business’s needs.
This wasn’t a commercial project—instead, it was a learning exercise to explore how AI agents can support decision-making. Here’s how I approached it from concept to deployment.
1. Mapping the User Experience
Before writing any prompts or training the model, I began by designing a process map.
My goal was to understand:
- What questions the bot should ask
- How the decision logic would work
- How the AI would interpret user inputs
- The flow that would guide the user from start to recommendation
This step helped establish a clear, structured user experience and ensured the agent followed a consistent logic path when evaluating CPQ options.
2. Collecting Training Data
Next, I gathered the content needed to train the AI agent. To keep the model focused and effective, I separated the training data into two categories:
A. CPQ Platform Selection Criteria
This included details such as:
- Core features
- Integration capabilities
- Scalability
- Pricing structures
- Industry alignment
- Strengths and weaknesses of leading CPQ solutions
B. CPQ Business Benefits
This covered:
- Efficiency gains
- Quote accuracy improvements
- Reduced sales cycle times
- Improved customer experience
- ROI considerations
Splitting the data this way helped the AI understand both how to choose the right CPQ platform and how to articulate the value of that choice.
3. Choosing a Simple AI Platform (Chatbase.io)
To build the first version of the chatbot quickly, I chose Chatbase.io.
Key reasons:
- Fully automated setup
- Very easy to train
- No coding required
- Provides an embeddable widget for websites
It was an ideal environment for rapid prototyping.
4. Training the AI Agent
With the materials ready, I uploaded both data sets into Chatbase.
The agent was configured to:
- Ask the right discovery questions
- Compare CPQ platforms
- Align recommendations with user needs
- Communicate potential benefits clearly
The platform handled the structuring and indexing, making the training phase fast and intuitive.
5. Testing for Accuracy and Reducing Bias
I ran multiple test scenarios to evaluate whether the agent would:
- Choose the correct CPQ platform
- Follow the decision logic consistently
- Avoid bias toward any specific vendor
- Explain its recommendations in a practical, business-friendly way
This iterative testing helped strengthen reliability and eliminate gaps in the logic.
6. Embedding and Publishing the Chatbot
After validating the results, I embedded the chatbot into my website using the script provided by Chatbase. This required only a simple copy-and-paste into my webpage—no development work needed.
Once published, the bot became accessible as an interactive tool that others could explore.
Closing Thoughts
This project gave me valuable insights into how AI agents can support software evaluation processes like CPQ selection. Even a simple prototype can demonstrate how AI:
- Enhances decision-making
- Guides users through structured logic
- Reduces manual research time
- Makes complex evaluations more accessible
Most importantly, it showed how approachable AI development can be with the right tools—making hands-on experimentation one of the best ways to learn.
If you’re interested in building your own AI-driven prototype or want guidance on exploring CPQ solutions, I’m always happy to share what I learned.