Building Our AI Tool: Behind the Scenes

In a world where speed, precision, and personalization are paramount, we set out on a mission: to build an AI tool that revolutionizes how businesses configure, price, and quote — especially within complex systems like Oracle CPQ.

But the journey wasn’t just about writing code. It was about solving real problems, rethinking user experience, and reimagining what’s possible with AI.

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The Spark: Why We Built It

Our team noticed a consistent pain point across enterprises using Oracle CPQ: time-consuming manual configurations, scattered data, and repetitive pricing logic that led to bottlenecks. Sales cycles were slower. Accuracy was inconsistent. Opportunities were missed.

We asked a simple question:
What if AI could do the heavy lifting — and let teams focus on what they do best?

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Designing the Core Intelligence

Our AI model wasn’t just trained on data — it was shaped by real-world use cases. From day one, we collaborated closely with CPQ architects, sales engineers, and pricing analysts. We observed how they worked, where they got stuck, and what decisions they made every day.

Then we translated those behaviors into algorithms.

We built:

• Smart Config Recommendations based on prior selections and usage patterns
• Dynamic Pricing Engines that adapt in real-time to margins, competitors, and region
• Commerce Rule Automation to eliminate repetitive logic and manual updates

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Challenges We Overcame

Building a tool that works seamlessly with the requirements of  Oracle CPQ wasn’t easy.

• We faced many complexities
• We wrestled with user adoption – making AI feel more like a partner than a black box
• And we had to ensure everything was secure, fast, and scalable

The biggest challenge? Earning user trust.
That meant transparency, auditability, and the ability to override AI suggestions — always putting the human in control.

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The Breakthrough Moment

After months of development and iteration, something clicked.
Our pilot users started completing quotes 70% faster, with error rates cut in half. They weren’t just using the tool — they were relying on it.

“I used to dread pricing complex bundles,” one user told us. “Now I get suggestions before I even ask. It’s like having a CPQ expert sitting next to me.”

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What’s Next?

This is just the beginning.
We’re exploring new capabilities like:

• Natural language input (“Create a quote for the west region using standard discount rules”)
• Deeper analytics to predict quote outcomes
• Cross-platform insights across CRM, ERP, and CPQ

We believe AI isn’t here to replace people — it’s here to empower them.
And building this tool has been a step toward making enterprise selling smarter, faster, and more human