Turn RFQ Attachments Into Quotes Faster

Every RFQ lands with a bundle of attachments: a STEP file for the geometry, an STL for the additive build, a PDF drawing with GD&T callouts, a spreadsheet BOM, and maybe a Word doc listing post-process requirements. Before your estimator can even start pricing, someone has to open each file, verify the revision, extract critical dimensions, confirm material and finish, then key it all into the quoting tool. Multiply that by twenty RFQs a day and the bottleneck is obvious.

Why file intake slows you down

Manual file handling creates three compounding problems:

• Time sink: Opening, rotating, measuring, and transcribing data from CAD and drawings takes 15–30 minutes per quote.
• Errors: A missed tolerance, wrong material grade, or overlooked secondary operation flows straight into the quote — and later into a margin-eroding change order.
• Inconsistency: Different estimators interpret the same files differently, so two quotes for the same job show different lead times and prices.

Shops that move the intake step from human hands to automated pipelines see quoting times drop from hours to minutes — exactly the shift documented in case studies where 24-hour turnarounds became sub-five-minute quotes.

What an automated intake pipeline looks like

You don’t need a custom AI project. Modern quoting platforms ingest the most common manufacturing file types and surface the data your estimators actually need:

• STEP / IGES / Parasolid: Extract volume, surface area, bounding box, mass (with density), hole counts, and feature recognition for setup estimation.
• STL / 3MF / OBJ: Compute build volume, support volume estimate, layer count proxy, and nesting bounds for powder-bed or material-extrusion processes.
• 2D PDFs / DXF / DWG: OCR title blocks for part number, revision, material, finish, heat-treat, and critical tolerances; vector geometry for flat-pattern or waterjet/laser nesting.
• BOM spreadsheets / CSV: Map columns to your quoting line items — quantity, material, process, post-process — so multi-part RFQs become a single quote build.

The output is a structured data object your quoting engine can consume directly: part geometry attributes, material options with stocked grades, process routing suggestions, and flagged DFM issues (thin walls, unprintable overhangs, unreachable features).

Keep the human where judgment lives

Automation should stop at the decision boundary. Let the software:

• Parse files and pre-fill every field it can with high confidence.
• Surface uncertainties — unreadable callouts, conflicting revisions, missing material certs — as review tasks for the estimator.
• Apply your shop’s pricing rules, setup-time models, and capacity calendars to generate a first-pass quote.

Your estimator then spends their time on the high-value decisions: adjusting for fixture complexity, selecting the optimal machine slot, negotiating lead time against current load, and adding the commercial terms that win the order.

Build a feedback loop that sharpens the engine

Every quote that ships — won or lost — carries signal. Capture the delta between the auto-generated first pass and the final quoted price, lead time, and process selection. Feed those deltas back into:

• Setup-time models: Calibrate per-machine, per-material, per-complexity tier.
• Material yield factors: Update nesting efficiency and scrap rates from actual build data.
• DFM rule weights: Promote or demote auto-flagged issues based on real manufacturability outcomes.

Over a few months the first-pass quote becomes so accurate that estimators approve it with minor tweaks — turning a 30-minute job into a 3-minute review.

Integrate with the rest of the workflow

File intake doesn’t end at the quote. The same parsed data should flow downstream:

• MES: Geometry attributes and routing decisions auto-create work orders with correct machine assignments and setup instructions.
• Job board: If capacity is tight, push the structured RFQ to trusted partner shops with all files and specs pre-packaged — no re-entry, no email chains.
• ERP / CRM: Attach the parsed BOM and spec sheet to the opportunity so purchasing and scheduling see the same truth.

This end-to-end continuity is why shops using a unified quoting + MES + job board platform report higher capacity utilization and additional revenue from overflow work they can now accept and fulfill reliably.

Start with the next RFQ

You don’t need a migration project. Pick the next ten RFQs, run them through an automated intake tool, and measure: minutes to first-pass quote, fields pre-filled vs. manually entered, and estimator review time. The numbers make the case for rolling it out across the whole funnel.

Solvi’s instant quoting engine is built around this exact pipeline — parsing CAD, mesh, drawing, and BOM files, applying your pricing logic, and pushing structured data straight into MES and the job board. See how it works at https://www.solvi.io.