Salesforce CPQ has long been the foundation for quoting operations across industries. But as Salesforce shifts focus from its legacy CPQ offering to Revenue Cloud, the landscape is evolving.
Revenue Cloud brings scalability, native integration, and modern architecture – but quoting workflows still suffer from one critical gap: real-time intelligence.
Market Challenge
Despite new products, many sales reps continue to juggle disconnected tools to finalize quotes and are unable to leverage the intelligence within Salesforce and their enterprise databases within the context of the quoting activity. The result? Hundreds of product rules, pricing rules, complicated approval matrices, and not being able to take external market factors such as interest rates, oil prices, or currency fluctuations.
This leads to quoting on a sliver of the data, slower cycles, inconsistent pricing, and missed opportunities. A recent client we worked with had over 350 active product rules to manage product compatibilities/recommendations and over 500 Advanced Approval rules to prevent high discounts, low-margin products, short contract term duration, excessive free periods, and reduce renewal churn.
Agentic AI: A New Paradigm
Agentic AI – powered by large language models (LLMs) – offers a way forward. Unlike rule-based engines, these models adapt to context. When embedded inside Salesforce CPQ or Revenue Cloud, they can:
- Recommend complementary or substitute products by populating the quote with products added to a new group titled “Product Recommendations”.
- Dynamically predict win probability based on discounting and prior Win/Loss history.
- Automate approvals (while also notifying the approver) based on historical approvals.
- Flag risks in real time by including data from the ERP, such as outstanding invoices.
Embedding this intelligence directly within the quote-building interface turns the Quote Line Editor from a static data table into a proactive advisor.
What It Could Look Like
Imagine a sales rep configuring a quote in Revenue Cloud. Instead of tabbing to external tools, they see a side-tab within the Quote Line Editor offering real-time recommendations, such as:
- “Add Firewall X to complement Server A”.
- “Your gross margin is below threshold – consider reducing the discount by 5%”.
- “Quotes like this closed 30% faster when paired with Support Bundle Y”.
These insights are powered by live quote data, enriched with previously seeded product logic and deal outcomes, and returned instantly by an LLM such as OpenAI or xAI Grok.
Example Use Case
Let’s take the example of having intelligent product recommendations and pricing to power faster quoting velocity and a higher closed/won probability.
A sales rep starts quoting a “Cloud Server – Basic” for a customer. As soon as the item is added, information about the item from the Salesforce product records, industry, number of employees, and a summary of the existing assets and subscriptions is sent to a secure LLM.
The response from the LLM in this case is a proposal for a product specific to that industry, as well as a compatible firewall and an extended warranty plan (at a price) based on historical sales and the highest close/win probability. This information can be surfaced in a number of different ways, including an LWC.
The key thing to note here is that the pricing recommendations are also taking into account external data sources such as inflation rate, tariffs, and other parameters that may affect the seller and the buyer over the term of the agreement. These suggestions, based on historical pairing data and seeded product rules, aim to improve both deal size and likelihood of closure while increasing the velocity of quoting
Why QLE+ is the Best Option
QLE+ is an AppExchange solution from B2B-Matrix that brings this vision to Salesforce CPQ and Revenue Cloud today. Built with Lightning Web Components, Apex, and Named Credential-secured API callouts, QLE+ has out-of-the-box integration with LLMs such as ChatGPT and supports the use cases outlined above
Solution Architecture Highlights:
- Salesforce-native UI: Built with LWC, styled with SLDS.
- Secure Integration: Uses Named Credentials for API callouts.
- Minimal Disruption: Insights appear in a tab beside existing quote lines.
- Performance Optimized: Payloads trimmed and cache-enabled to control costs and speed.
Final Thoughts
As quoting evolves from an operational task to a strategic function, embedding real-time intelligence inside Salesforce is the next logical step. Whether you’re maintaining legacy CPQ or transitioning to Revenue Cloud, agentic AI can help your team quote smarter, close faster, and sell more confidently.
Want to see what AI-powered quoting looks like in action? Learn more about QLE+ here or contact us directly to request a demo.
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