🛠️ Real Time Dynamic Quoting

Inspiration 💡

Many of our clients in the manufacturing industry struggle with the same pain point: real-time commodity pricing requires pulling data from multiple sources and a lot of manual input from employees. This inefficiency not only slows down workflows but also introduces risk of error in the quoting lifecycle.

We asked ourselves — what if AI could eliminate that manual burden? That's how Real Time Dynamic Quoting was born.

What It Does 🚀

Real Time Dynamic Quoting uses Agentforce to:

🔍 Generate a real-time pricing report from multiple data sources
🧾 Automatically create Salesforce records:
- Opportunities
- Quotes
- Quote Line Items
- Quote Documents
📎 Return a complete quote with all related records for users to review and interact with
🧠 Respond to flexible, natural language prompts with context-aware logic

We designed the system to be modular and adaptable, ensuring it can fit into different quoting workflows — this version is a general use case, not tied to a specific proprietary system.

How We Built It 🧱

Powered by Agentforce with actions primarily written in Apex
Utilized Prompt Templates to guide agent responses where needed
Built a modular architecture for actions — making them reusable and more flexible
Integrated multiple pricing data sources directly into the flow

Challenges We Ran Into ⚠️

Dynamic Pricing Report Generation
Initially, the agent couldn't generate its own pricing report based on other actions’ output.
✅ Solved with Prompt Templates to guide structured report generation.
Overly Monolithic Create Quote Action
The first version tried to do everything in one step, which limited flexibility.
✅ Solution: Broke it down into smaller, modular actions that handle scenarios like:
- “Put all quotes under one opportunity.”
- “Add these prices to an existing quote.”

This gave the agent the intelligence to respond to specific user needs while still following the correct quote lifecycle.

Accomplishments We're Proud Of 🏆

Building a modular, intelligent agent capable of handling complex workflows
Delivering a solution that is both flexible and scalable for enterprise use
Demonstrating the real-world value of AI in automating and enhancing business operations

What We Learned 📘

Agentforce has evolved significantly — now capable of understanding complex prompts and resolving them with structured logic.
Agent consistency and reliability have improved dramatically with clear instruction sets and prompt templating.
The importance of modularity and context-awareness in AI-driven systems.

What's Next 🔮

This project is just the beginning. We're planning to:

Expand functionality and improve user interactions
Fine-tune the agent’s ability to adapt to more diverse quoting scenarios
Turn this into a reusable framework that we can implement across multiple clients and quoting platforms

By continuing to build on this foundation, we aim to transform how quoting is done in the manufacturing space — making it faster, smarter, and future-ready.