From RFQs to Design Labs to AI
The New Reality for Engineer-to-Order Manufacturers
If you’ve ever lived inside an engineer-to-order sales cycle, you know the pain.
A customer sends an RFQ. You forward it to engineering. Weeks later, drawings come back, questions fly, things change, everyone scrambles.
That process isn’t broken, it’s just dated.
Customers no longer want to wait. They want to see what’s possible, tweak it, and get an answer now.
That’s why so many manufacturers are moving toward Design Labs - digital spaces where customers can explore, visualize, and configure products themselves.
Now there’s a new twist: AI is joining the equation. And it’s quietly changing how we sell, quote, and build.
The Design Lab, evolved
A Design Lab lets customers co-create instead of request.
They can spin a 3D model, adjust options, get pricing, and even generate drawings without sending an RFQ into the void.
But the next evolution is an AI-assisted Design Lab - one that doesn’t just show options, but thinks alongside the user.
Here’s what that looks like in practice:
AI reads the spec. A customer uploads a drawing or a technical doc. The system scans it, extracts key parameters, and matches it against what you’ve built before.
AI classifies the opportunity. Is this a repeat customer who bought a similar system? Then reuse that configuration. Is it something new? Route it to engineering with context already filled in.
AI predicts feasibility. Before anyone touches CAD, the system can flag impossible combos or materials that will blow your lead time.
AI suggests pricing. It compares the spec against your past quotes, materials, and margins, then recommends a range.
AI learns. Every project feeds the next. Over time, the system becomes a library of proven designs, constraints, and patterns — turning tribal knowledge into searchable intelligence.
In short: you move from reacting to specs to understanding them instantly.
Why it matters
Speed. Customers get answers in hours, not weeks.
Focus. Engineers spend their time on real innovation, not near-duplicates.
Margin. AI flags risk and helps quote with confidence.
Experience. Customers feel guided, not gated.
And maybe most importantly: it builds trust.
When customers can visualize, configure, and get instant feedback, they stop seeing you as a black box. You become a partner.
How we help manufacturers get there
This shift isn’t just about technology, it’s about design thinking, process clarity, and cultural change.
Here’s how we guide companies through it:
1. Understand your data reality.
We start by auditing your specs, RFQs, and historical builds. How consistent are they? How searchable? The better the data, the better the AI.
2. Build a “minimum viable” Design Lab.
Pick one product family. Stand up a visual configurator + CPQ flow. Add a lightweight AI layer to classify specs and flag risk.
3. Connect the dots.
Integrate it with your CRM, CPQ, ERP, and PLM so the AI has full visibility into what you’ve sold, built, and delivered.
4. Measure the impact.
Track quote cycle time, engineering touches, rework rate, and customer satisfaction. Prove the value fast.
5. Scale and evolve.
Expand across product lines. Feed the AI new data. Refine rules. Watch your quoting and design velocity compound.
The bigger story
The best manufacturers aren’t automating to cut people, they’re automating to elevate them.
AI doesn’t replace engineers. It removes the noise so they can focus on what actually requires their expertise.
The winners will be the ones who combine human judgment and machine intelligence, turning RFQs into real-time collaboration.
That’s the future of ETO.
Not waiting for specs to arrive; but helping your customers design smarter, faster, and together.
Bottom line: If your RFQ queue feels endless, it’s time to stop waiting and start enabling.
AI won’t just read your specs… it’ll help you rewrite the playbook.