Overview
A large packaging equipment manufacturer wanted to take the manual work out of its aftermarket quoting process. Its Aftermarket team ran a two-week pilot of the AI Quote Agent from Entytle (acquired by RapidCanvas). The agent reads inbound quote requests, sends them to the right person, and matches customer and part details against the company's master data. Anything uncertain goes to a human to review.
Challenges
The Aftermarket team handled quoting across many customer locations and product lines. As the business grew, the work behind each quote grew with it:
- Hundreds of quote requests arrived by email every week, and each one had to be read and triaged by hand.
- Each request had to be assigned to the right team member by hand.
- Customer locations and material numbers had to be pulled from unstructured emails and checked against local Excel sheets, which was hard to do accurately.
- Time spent on triage was time not spent on follow-ups, proactive outreach, and strategic work.
With this much manual work, quotes took longer to turn around, errors were more likely to slip in, and the team had little capacity left for higher-value work.
Solution
The Entytle AI Quote Agent combines language models with business logic built for aftermarket quoting. In line with the RapidCanvas Hybrid Approach™, the agent does the repetitive work and people make the decisions. The agent:
- Routes and assigns each incoming request to the right team member within seconds
- Pulls customer locations and material numbers out of the email text and matches them to the company's installed base customer data and item master
- Flags open issues for a person to review, so accuracy and compliance stay in human hands
Results
The pilot ran in two rounds. The second round used an improved model and performed better than the first, which showed how quickly the agent can be tuned on real data.
| Metric | Result |
|---|---|
| Quote assignment | 100% assigned correctly |
| Customer location identification | 87% identified correctly |
| Material number matching | 76% matched correctly |
Items the agent couldn't match with confidence went straight to the team for review. Manual triage dropped sharply, which gave the team time back for customer-facing work.
The Business Impact
The Quote Agent automated a process, and it also opened up new possibilities:
- Faster quote turnaround supports a better customer experience and helps win more deals.
- Less manual effort lets experts focus on strategic, revenue-generating work.
- The agent scales and adapts, and it gets better as it learns from reviewed items.
As one industry leader put it:
“AI isn't about replacing people. It's about giving them superpowers to focus on what matters most.”
Conclusion
The pilot showed that the AI Quote Agent can take on high-volume, repetitive triage while the team keeps control of every quote. The next step is broader rollout. Areas to explore include raising material-match accuracy as the model learns from reviewed items and extending the agent to quote follow-ups.
