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How Montanstahl uses an AI RFQ agent to qualify complex requests in seconds

At a glance

  • Customer: Montanstahl AG – Swiss producer of stainless and special steel profiles

  • Use case: AI agent for RFQ feasibility, alternatives & lead times

  • Data used: 27+ months of RFQs, quotes, drawings, emails and specs

  • Where it lives: Inside the tools sales already use for RFQs

  • Impact: >85% accurate feasibility suggestions; feasibility answers now in seconds instead of hours

The Business Challenge: The "Feasibility" Bottleneck

For Montanstahl, the hard part of an RFQ wasn’t writing the quote – it was answering a more fundamental question:

 

“Is this technically feasible for us, and if not, what can we offer instead?”

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Every complex request kicked off a slow, manual process:

  • Complex feasibility analysis
    Before a quote could even start, sales managers had to investigate:
    Can we manufacture this geometry? In this grade and tolerance? What lead time is realistic? What close alternatives can we suggest?

  • Knowledge silos
    The answers lived in 27+ months of unstructured emails, drawings, spreadsheets and internal notes. Getting to a clear “yes/no” depended entirely on who you knew and how quickly they replied.

  • Slow customer response
    This “go / no-go” step was the real bottleneck. It delayed responses to customers and increased the risk of losing opportunities to faster-moving competitors.
     

The C-suite goal was clear:

 

Automate as much of the feasibility step as possible before quoting, without disrupting existing tools or processes.

The Solution: An AI Agent  for Technical Sales Qualification

Mavlon was selected to build an AI-Sales Agent designed for one primary purpose: to provide the sales team with instant, accurate answers to "Can we do this?"
 

This was a fundamental re-engineering of the pre-sales workflow:
 

  1. From hours to seconds: Mavlon’s proprietary AI algorithms processed and understood 27+ months of technical sales emails, product specs, and past inquiries. This data was transformed into an intelligence layer capable of understanding complex technical RFQs and instantly determining product feasibility, viable alternatives, and availability.
     

  2. Seamless Workflow Integration: The AI agent was embedded natively within the sales team's existing communication tools. This strategic, "no-friction" approach drove 100% adoption. Sales managers now receive immediate, accurate feasibility answers directly within their standard workflow, eliminating the need to switch applications.
     

  3. A "Data-First" Security Model: Trust and data sovereignty were paramount.

    • 100% Data Ownership: Montanstahl retains full control over its proprietary knowledge base.

    • Total Privacy: Their data is never used to train public models, ensuring full GDPR compliance and protecting their competitive advantage.

Results & Business Impact

The solution delivered immediate, measurable ROI by directly impacting the sales qualification pipeline.
 

  • Met >85% Accuracy Benchmark in AI-Driven Feasibility Analysis: This was the primary C-suite metric. The AI agent proved it could reliably parse complex technical inquiries (e.g., "Can we offer product X with Y specification?") and generate accurate, verifiable answers (e.g., "No, but we can offer product Z as an alternative, available in Q3.").
     

  • Drastically Accelerated RFQ-to-Quote Velocity: What previously took hours of manual, internal investigation is now an instant, AI-generated analysis. This massive "time-to-answer" reduction allows the sales team to qualify more leads and move viable RFQs to the final quote stage faster than competitors.
     

  • From "Tribal Knowledge" to Strategic Asset: Montanstahl’s institutional knowledge is no longer a liability locked in inboxes. It is now a secure, strategic asset that actively informs automated decisions and ensures 100% consistency in all technical communication and sales-quoting decisions.
     

  • Accelerated Sales Onboarding:  New hires are no longer dependent on senior staff. They use the AI agent as their "personal expert," allowing them to become productive and confidently handle complex RFQs in weeks, not months.

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This project demonstrates Mavlon's ability to deliver secure, high-impact AI solutions that automate complex, knowledge-based decisions, turning enterprise data into a decisive competitive advantage.

What this means for other manufacturers?

This project shows how a focused AI agent can turn a manufacturer’s own data into a practical advantage on every RFQ – without replacing ERP or CRM systems or forcing people into a new tool.

 

Montanstahl’s RFQ agent is a pattern:
ingest your historical RFQs + decisions → train an AI Agent → embed it where your sales engineers already work.

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Curious if this pattern fits your RFQ flow?

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I’m happy to walk you through Montanstahl’s setup and, in 20 minutes, we can see whether a similar agent makes sense for your team.
 

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