The question was always the same: can we actually make this?
How the Montanstahl Group turned years of RFQs, drawings, technical emails, and production data from three sites into an instant RFQ screening engine, embedded directly in the sales team's existing workflow.
The problem every subcontractor recognizes
Before a quote could even begin, someone had to answer: can we produce this geometry in the requested alloy and tolerance? Which site and route should we use? What is a realistic lead time? What alternatives can we offer?
For complex profiles involving tight tolerances, aerospace-grade titanium, or unusual geometries, those answers were buried in:
- Years of past RFQs, drawings, and technical emails scattered across three production sites
- Scattered spreadsheets and internal notes in Stabio, Schwerte, and Talamona
- The heads of a few senior engineers who remembered similar jobs from years ago
The multi-site structure made it worse.
- Montanstahl AG in Switzerland handles cold-rolled and laser-welded profiles
- Montanstahl GmbH in Germany does hot extrusion in nickel and titanium
- Siderval in Italy produces hot-extruded seamless profiles in carbon steel, stainless, and superalloys
A single feasibility question often required cross-site phone calls that added days to what should have been a minutes-long decision.
Feasibility checks took hours instead of minutes while competitors responded faster. A handful of senior engineers became the bottleneck for every “can we make this?” question. New sales hires needed months before they could handle complex RFQs independently. High-value RFQs risked being delayed or dropped simply because feasibility was unclear.
The group's objective was simple: automate as much of the feasibility step as possible without asking sales to change their tools or workflow.
Instant screening of any RFQ
Mavlon's AI reads incoming RFQs and immediately determines: can the group produce this geometry, in this alloy, at this tolerance? Which production site is best suited? What is a realistic lead time? What alternatives exist if the exact request is not feasible? The system drew on the group's full production history and technical knowledge across all three sites, so a sales engineer in Switzerland got answers informed by Siderval's Italian extrusion capabilities without making a single phone call.
Zero-friction integration
The system was embedded directly into the team's existing communication tools. No new software to learn. No separate application to open. When an RFQ arrived, the feasibility answer appeared in the same workflow the estimator was already using. This drove 100% adoption from day one.
Full data sovereignty
The group retains full ownership and control of their proprietary data. Nothing is used to train external models. Full GDPR compliance.
85%+ screening accuracy
For 85% of incoming RFQs, the AI suggestion was directly usable or needed only minor adjustment. The remaining 15% were genuinely novel requests that correctly routed to senior engineers for human judgment.
Seconds instead of hours
Standard feasibility checks that previously required emails, phone calls, and cross-site coordination were answered instantly, including which production site was best suited for the job.
Thousands of senior hours freed per year
Repetitive “can we make this?” questions stopped reaching senior engineers. Estimated annual business impact from capacity gains and higher win rates: low seven-figure range.
New hires productive in weeks, not months
New sales staff queried the system as their on-demand expert instead of spending months absorbing tribal knowledge through questions and osmosis.
Consistent answers across all three sites
The same question asked by different people on different days produced the same answer. A sales engineer in Switzerland could instantly access Siderval's Italian production history without a single phone call.
“Experienced people answering feasibility one RFQ at a time, while the queue grows. Different questions. Same bottleneck.”
The same bottleneck exists in every custom manufacturing operation.
The specifics change, the problem does not. An aerospace machine shop asks: can we hold these G6 tolerances? Do we have honing capability? Do we need to subcontract the anodize? A Boeing subcontractor asks: do we have CMM capacity for full positional verification on classified coordination holes? Have we worked with this datum structure before?
Mavlon's work with the Montanstahl Group proved this bottleneck can be automated with high accuracy. The approach has since expanded to direct extraction from technical drawings: GD&T, blanket tolerances, fit classes, surface treatment specs, and pre-populated AS9102 FAI reports, all from a single uploaded PDF. The same engine now runs as the Estimating Agent and the Inspection Agent.
Ready to eliminate your quoting bottleneck?
Upload a drawing and see what Mavlon extracts. Or book a call to discuss how the approach applies to your shop's RFQ workflow.
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