AI for Engineering Productivity
AI for should-cost estimation gives engineers early cost feedback from CAD, so the most expensive design decisions are caught while they are still cheap to change.
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8 min read
Dr. Maor Farid
Maor Farid is the Co-Founder and CEO of Leo AI, the first AI platform purpose-built for mechanical engineers. He holds a PhD in Mechanical Engineering and completed postdoctoral research at MIT as a Fulbright fellow. A Forbes 30 Under 30 honoree and former AI researcher and mechanical engineer in an elite military intelligence unit, Maor leads Leo AI in its mission to help engineering teams design better products faster.
BOTTOM LINE
Cost is largely decided during design, yet engineers usually get cost feedback only after the design is frozen, which is the most expensive moment to learn what drove the price. That timing gap is where avoidable cost lives.
AI for should-cost estimation closes it by reading the model and returning an early, feature-based estimate while the design is still soft. It shows the cost drivers, supports reuse over new parts, and turns cost into a design parameter rather than a late surprise.
When evaluating a tool, look for estimates from real geometry, visible cost drivers, early feedback, and grounding in your own materials and parts. The point is a directional number early enough to change the decision.
Most of a product cost is decided in the first weeks of design, long before anyone sees a quote. By the time purchasing comes back with a number, the geometry is frozen, the tolerances are set, and the expensive choices are already baked in. Engineers are then asked to cut cost from a design that was never costed while it was still soft.
AI for should-cost estimation closes that gap. It reads the model and gives an early, defensible estimate of what a part ought to cost, while the design can still change cheaply. This guide explains what should-cost means, where AI helps, and how early cost feedback changes the decisions engineers make.
Why Cost Is Decided Long Before the Quote
A widely cited reality of product development is that the large majority of a part cost is committed during design, even though most of the spending happens later. The choice of process, material, tolerance, and feature set sets the cost floor. Once those are fixed, procurement is negotiating within a band the design already defined.
The problem is timing. Engineers rarely get cost feedback until a supplier quotes the finished design, which is the most expensive moment to learn that a tolerance or a pocket drove the price. Earlier cost visibility would change decisions, the same way early manufacturability feedback does in DFM analysis.
The asymmetry is stark. A decision that takes an engineer a minute, rounding a tolerance or choosing a stock size, can move the part cost by a wide margin once it is multiplied across a production run. Yet that engineer makes the decision blind to cost, because the only cost signal in the building arrives weeks later and lands on someone else is desk.
IN PRACTICE
What Engineers Are Saying
"Leo found a nature-inspired solution, a concept we would not have thought of, that let us use standard, off-the-shelf parts. No custom manufacturing. No dedicated engineer. We saved around $400 per system."
Chen, Team Lead at ZutaCore
What a Should-Cost Estimate Actually Is
Should-cost is a bottom-up model of what a part ought to cost based on its features and the process that makes it, rather than what a supplier happens to charge. For a machined part, that means estimating material, the cutting time to create each feature, non-cutting and setup time, and overhead.
Built well, a should-cost gives engineers and buyers a defensible baseline: a number grounded in the physics of making the part. The hard part has always been speed. Producing a feature-by-feature estimate by hand is slow, so it is reserved for a few high-value parts and skipped for the rest, where cost quietly accumulates.
A should-cost also reframes negotiations. When a buyer holds a defensible bottom-up estimate, the conversation with a supplier shifts from accepting a number to understanding it. If a quote sits far above the should-cost, that gap is a question worth asking, and often it points back to a design feature the engineer can change rather than a price the buyer must simply accept.
How AI Brings Cost Into the Design Loop
AI changes the economics of estimating. By reading the CAD model directly, it can identify the cost-driving features, estimate process time, and return a should-cost in the design tool, not weeks later. This is where Leo AI fits: it reads native CAD geometry and understands features in context, so an engineer can ask what is driving the cost of a part while there is still time to act.
That serves the design-productivity value driver directly. When an engineer sees that a single tight tolerance or a deep pocket doubles the cost, they can relax it, change the approach, or confirm it is worth the price. Cost stops being a surprise from purchasing and becomes a design parameter, alongside the kind of trade-offs covered in tolerance analysis.
The feedback loop is what matters. An estimate that arrives in the design tool, in the moment, lets an engineer test alternatives: a looser tolerance, a different stock form, a simpler feature. Each becomes a small experiment with a visible cost answer, so the design converges toward a number the team chose rather than one it inherited.
The Cheapest Part Is One You Already Make
There is a cost lever that estimating alone misses: reuse. The cheapest part is often one the company already qualified and buys in volume, not a new design that needs its own tooling, qualification, and supplier setup.
Because Leo AI can find existing parts by geometry and intent, it lets an engineer check whether a near-equivalent already exists before committing to a new, separately-costed component. Avoiding a redundant part removes its entire cost tail, which is why part reuse is one of the highest-return cost moves a team can make.
Reuse compounds with cost visibility. When an engineer can both see the cost of a new part and instantly check whether a qualified equivalent already exists, the cheaper path is usually obvious. The combination turns two separate good habits, costing early and reusing parts, into a single fast check at the moment of design.
What to Look for in AI Should-Cost
A useful cost tool fits the design workflow and earns trust.
1. Reads the model It should estimate from the real geometry and features, not a manual re-entry that goes stale.
2. Shows the cost drivers It should say which features and tolerances drive the number, so the engineer knows what to change.
3. Early and fast It should return a useful estimate during design, not as a slow gate at the end.
4. Connected to your data It should reflect your materials, processes, and existing parts, not a generic catalog.
The goal is not a perfect quote. It is a fast, directional number, early enough to change the decision that sets the cost.
None of this asks engineers to become cost accountants. It asks the tool to put a directional number where the decision is made, with the drivers visible, so cost becomes one more property an engineer can see and shape, alongside stress, weight, and fit.
FAQ
Cost Your Design While It Is Soft
Cost feedback arrives after the design is frozen. Too late.
Leo AI reads your CAD model, estimates the should-cost early, and shows which features drive the number, while you can still change it.
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