There’s a temptation, when building any kind of quoting software, to keep adjusting the pricing logic. To tweak it. To optimise it. To “AI-enhance” it. To add layers of abstraction that promise intelligence but often end up hiding the fundamentals.
We are not doing that.
The pricing strategy inside our Instant Quote platform is not a temporary mechanism. It is not a placeholder. It is not something we will replace once something “smarter” comes along.
It is the core.
And if we are serious about building something that surpasses platforms like AMFG in real-world usability, then keeping that pricing engine intact is not a limitation — it is a strategic advantage.
Let me explain why.
Pricing Is Not Just a Calculation — It’s a Manufacturing Model
Many online quoting tools are, in truth, surface-level calculators. They give the appearance of precision, but underneath they often rely on simplified heuristics or opaque internal logic that the end user cannot see, verify, or understand.
Our system is different.
The pricing model is:
Geometry-based
Density-aware
Runtime-informed
Multiplier-controlled
Tenant-configurable
Deterministic
That means the quote is not pulled from a lookup table. It is derived from the actual characteristics of the part.
When a user uploads a model, the system extracts:
Bounding box dimensions
Volume
Mesh complexity
From that, we calculate material usage based on density. We model machine time. We apply overhead. We apply margin. We generate a unit price. We scale linearly for quantity.
There is no mystery layer.
And that matters.
Because when you’re quoting manufacturing — not selling t-shirts — credibility is everything.
Unit Price Stability Is Not Optional
One of the most important rules we’ve locked into this system is this:
The unit price remains stable.
The total scales linearly with quantity.
This sounds obvious, but you would be surprised how many systems distort pricing when quantity changes.
If the same part costs £12 per unit at quantity 1, it should still be £12 per unit at quantity 10 — unless there is a clearly defined setup amortisation strategy. Hidden recalculations destroy trust.
Engineers notice. Buyers notice.
Consistency builds confidence. And confidence wins business.
This rule stays.
Transparency Is the Differentiator
If we want to surpass AMFG — and that is the current strategic target — we do not need to reinvent pricing.
We need to expose it intelligently.
Most enterprise quoting platforms abstract away the pricing logic. They present a number. Perhaps a lead time. Maybe a breakdown.
But rarely do they allow the user to understand the structure behind the quote.
We can.
Imagine a collapsible “How This Price Was Calculated” section that shows:
Estimated material weight
Estimated machine time
Setup allocation
Applied margin
Total calculation
Not as marketing fluff — but as structured engineering transparency.
For SMEs, this is powerful.
For engineers, this is reassuring.
For procurement, this is defensible.
And we can do this without changing the pricing formula at all.
Surpassing AMFG Doesn’t Require AI
There is a misconception in the manufacturing software space that “surpassing” a competitor means adding complexity.
It doesn’t.
AMFG is strong in workflow automation and backend enterprise integration. But their public-facing experience is not polished for small to mid-sized service bureaus. It’s structured. It’s capable. But it’s not lean.
We win by being:
Cleaner
More transparent
Easier to deploy
Simpler to configure
More trustworthy
Not more complicated.
We do not need to introduce black-box predictive pricing.
We do not need to compromise deterministic logic.
We do not need to sacrifice explainability.
What we need is refinement.
Intelligence Around Pricing, Not Inside It
The pricing engine remains untouched.
But we can build intelligent layers around it.
For example:
Risk Scoring Layer
Without modifying the base formula, we can introduce a structured risk assessment:
Low Risk
Moderate Risk
High Risk
This can be based on:
Thin wall detection
Aspect ratio thresholds
Build volume proximity
Process-specific geometry concerns
If needed, a risk multiplier can be applied:
Core Price × Risk Multiplier
The underlying pricing logic remains intact.
The enhancement is modular.
That is how you evolve a system without destabilising it.
Post-Processing Add-Ons
Instead of embedding complexity into the core pricing engine, we introduce selectable modules:
Cosmetic finish
Engineering finish
Dimensional inspection
Thread tapping
Insert installation
Each option applies a clear, separate modifier.
Again:
Core pricing remains stable.
Extensions are transparent.
Delivery Modifiers
Standard. Priority. Express.
Each implemented as a multiplier on the total.
No hidden recalculation. No override at checkout. No Stripe-side manipulation.
Everything flows from the signed quote token.
Integrity remains preserved.
Why Deterministic Pricing Is a Strategic Asset
When you scale into a multi-tenant SaaS model, pricing discipline becomes more important — not less.
Tenants need to:
Control material cost
Control machine rates
Adjust overhead
Adjust margin
But they need that within a predictable framework.
If pricing becomes fluid, experimental, or opaque, you create support overhead. You create disputes. You create instability.
A deterministic pricing engine is:
Easier to document
Easier to defend
Easier to audit
Easier to support
That is not glamorous. But it is commercially powerful.
The Real Goal
We are not trying to be the most complex quoting engine in the world.
We are building:
A geometry-aware, machine-constrained, transparent, multi-tenant additive manufacturing quoting platform.
If we:
Refine the public UX
Add manufacturability confidence indicators
Introduce structured risk levels
Improve onboarding simplicity
Provide clear price breakdown transparency
Then we surpass AMFG in the areas that matter to SMEs and independent service bureaus.
Without touching the core.
The Spine Stays
The pricing engine is the spine of this system.
It is geometry-driven.
It is density-aware.
It is runtime-informed.
It is multiplier-controlled.
It is tenant-configurable.
It is signed and verified.
That does not change.
Everything else evolves around it.
If we stay disciplined — and resist the urge to redesign what already works — we will build something stronger than enterprise-heavy portals and more trustworthy than opaque AI-driven quote engines.
And in this industry, trust wins.
That is the direction that we will always take