AI Chatbot for Heating & Renewable Energy Companies

// TL; DR

The deployment of conversational assistant from AddProcess reduced customer response times by more than 50%, significantly improving efficiency across high volume of inquiries.

Background

Digital transformation in home services is accelerating but in regulated industries like heating and renewable energy installation, AI must be precise, predictable, and compliant.

The company needed more than a generic chatbot. They needed a controlled conversational system that could qualify leads, guide customers, and integrate directly with Microsoft Dynamics 365 without relying on open internet knowledge.

Here’s how the solution was designed.

The Challenge: High Enquiry Volume, Complex Qualification

The company manages enquiries across boiler installations, renewable energy systems, maintenance & support, technical documentation requests and quote qualification. Manual qualification along with inconsistent data capture was slowing the team down. Routing enquiries internally was inefficient and generic AI tools were too risky for a regulated industry.

The Approach: Dual-Mode Conversational System

We designed a chatbot with two integrated modes:

1️⃣ Conversational Mode

• Interprets customer intent
• Uses only company-provided literature
• Asks follow-up questions

The conversational layer guides users naturally into appropriate pathways such installation enquiries and support requests.

2️⃣ Scripted Mode

• Uses deterministic decision trees and fully controlled logic
• Structured lead capture funnels
• Qualification workflows

This layer uses structured internal documentation and decision trees only. No open internet access, no external knowledge sources. It was important to create a structured front-end qualification engine not "creative" AI assistant.

Two Architecture Approaches

• Microsoft Copilot Studio with restricted knowledge sources
• Private LLM with RAG using a closed vector database

Both approaches ensure answers come exclusively from approved documentation.

Microsoft Ecosystem Integration

AddProcess integrated the chatbot directly into the company's Microsoft stack via Microsoft Dynamics 365 (Dataverse), used Power Automate to trigger workflows and Power Pages for frontend integration. The company added SMS notifications to ensure smooth communication when an inquiry is placed.

How the Chatbot Routes Customers

Here's how a typical journey works:

1. A customer lands on the website.
2. The chatbot interprets intent.
3. The system switches into scripted qualification mode.
4. It asks structured follow-up questions.
5. Data is written directly into Dynamics 365.
6. Power Automate triggers internal workflows.
7. The enquiry is routed to the correct department automatically.

For support enquiries, the chatbot provides literature-based answers first before escalating.

Business Impact

This structured conversational system delivers measurable advantages such as faster lead qualification and higher data accuracy. Analytics showed 20% rise in customer satisfaction as response time got shorter and the amount of support tickets decreased.

Why Controlled AI Matters in Regulated Industries

In sectors like heating, renewables, and home energy systems, incorrect advice can create compliance issues, damage brand trust and lead to costly errors.

By building a closed, literature-driven conversational system, the company ensured more predictability and complience. This isn't chatbot hype. It's structured conversational process automation.

The AddProcess Approach

At AddProcess, we focus on process-first automation, controlled AI architecture, microsoft-native integrations, deterministic conversational systems. The goal isn't to build "smart" chatbots. It's to build reliable, measurable business systems.

If you're exploring structured conversational AI solutions, we'd be happy to discuss how a controlled approach can support your operations.