The Solution

thinkbridge designed a future-forward AI Pricing Assistant that seamlessly integrated into the existing SaaS platform while introducing a modern, chat-based experience for field users. Here’s how we did it:

• Conversational Field Interface: We built a lightweight, intuitive mobile interface where technicians could describe jobs conversationally (e.g., “replace a 3-ton condenser, new lineset”). The app required zero training and worked in real-time, eliminating menu navigation or code searching.

• NLP-Powered Query Understanding: We integrated large language models—GPT-4 and Claude Sonnet—to interpret technician inputs. These models converted plain language into structured queries using OpenAI’s embedding models, ensuring contextual accuracy and understanding of nuanced trade language.

• Retrieval-Augmented Generation (RAG) Architecture: To ensure that sensitive data never left the source, we implemented a RAG pipeline that securely queried pricing logic in the backend. The AI engine retrieved and synthesized relevant pricing components without giving users access to the underlying pricing tables or decision trees.

• IP Protection by Design: By decoupling front-end interactions from backend exposure, we ensured that technicians could never download, scrape, or replicate pricing data. The system only ever returned the final price, not how it was derived—ensuring full intellectual property control.

• Plug-and-Play Scalability: Our modular architecture allowed the client to roll out the assistant selectively—by customer, region, or vertical—allowing for controlled testing and scaling across thousands of technicians in the field.