Raw Inputs
Your data, as it is
Engineering, transactional, contractual, and market inputs — loaded as-is, no manual reformatting required.
MaterialX Platform
Contextual cost intelligence for the AI era.
Every procurement and engineering team is trying AI. The ones getting real value gave it something to reason over — a live, structured layer that connects every cost driver, contract clause, and market signal back to your decisions. That's contextual cost intelligence, and that's what MaterialX is built to be.
Works with BOMs · CAD files · POs · Contracts · Commodity pricing · Indices · FX
MaterialX
MaterialX is a contextual cost intelligence platform. It turns your fragmented procurement, engineering, supplier, and market data into a live, structured intelligence layer — then into deep cost intelligence your team can act on. Speed to insights.
Contextual Intelligence Layer
Structured. Connected. Live.
AI-assisted structuring extracts information from the raw data and assigns every input its meaning — what it is, what it depends on, what it affects. The Indexing Engine maps cost drivers and formulas, propagates changes automatically, and timestamps every value.
Grounded AI Assistants
Grounded reasoning
AI assistants reason over the live layer rather than retrieve from documents — answering complex questions in natural language, with structured, explainable answers your team can defend.
Hero capability
Deep cost intelligence your team can act on.
Drop in a CAD file, a part spec, or a BOM. AI-assisted structuring interprets it alongside supplier quotes, contract terms, commodity pricing, and indices, and builds a live should-cost model decomposed driver by driver. The intelligence layer keeps it current as cost drivers change, and answers what's behind a price move, how a supplier compares to the market, what a design or material change does to cost, and the impact of what-if scenarios — grounded in your live data, traceable to source.
- Should-cost
- Cost-driver decomposition
- Supplier vs market
- Scenario impact
The difference
Built for cost, bottom-up. What sets MaterialX apart isn't a feature list — it's a set of design commitments at the foundation: how data is indexed, how logic stays current, how AI is grounded in your procurement and engineering context.
Indexed intelligence layer
A live, indexed cost intelligence layer that procurement and engineering teams — and AI — can all reason over.
- A cost-intelligence fabric, built from fragmented procurement and engineering inputs.
- Formulas, drivers, relationships, and history connected in one maintained layer.
- A live system, not a one-time analysis artifact.
Indexing Engine
Operationalizes your cost logic — relationships stay current, value changes propagate automatically.
- Maps relationships across parts, suppliers, formulas, and drivers.
- A live operational model, not a static report.
- Updates propagate across related entities automatically.
AI-assisted structuring
AI builds and maintains the intelligence layer itself, before any assistant runs over it.
- Interprets fragmented inputs and turns them into indexed cost context.
- Supports formula creation, value justification, and mapping maintenance.
- Keeps the model current as source values change — not a one-time setup.
Grounded AI assistants
AI assistants reason over the indexed intelligence layer, not disconnected data.
- Reasons over formulas, relationships, and live drivers — not just text snippets.
- Built for explanation and decision support, not generic chat.
- Every answer traces back through the maintained logic.
What you can do
MaterialX makes AI reasoning useful over your actual procurement and engineering context — not generic text. CPOs, CFOs, category managers, buyers, and cost engineers — receive grounded, explainable, and justifiable answers to the questions that matter to their work.
What changes
With a live contextual intelligence layer to reason over, the loop from signal to action changes shape.
Before
×
A commodity index shifts. Someone eventually notices.
×
An analyst pulls ERP data, cross-references the contract, digs into the supplier quote, rebuilds the formula in a spreadsheet.
×
A design changes. The cost engineer rebuilds the should-cost by hand — fresh quotes, redone every iteration.
×
The result: one person's answer, derived from one person's spreadsheet. Not shared. Not traceable. Not reusable.
×
Next cycle, the same question gets asked — and the work starts over from scratch.
Slow · Brittle · Unreusable
After
→
Index shift is reflected in the intelligence layer automatically.
→
Ask AI assistant: "Which supplier agreements are exposed to this movement, and by how much?"
→
Assistant highlights affected contracts, quantifies the exposure, and flags where should-cost diverges from what was paid.
→
A design change reprices the should-cost automatically, driver by driver — engineering sees the cost impact before the PO.
→
Answers are grounded, traceable, and available to the whole team. The next question builds on it.
Fast · Grounded · Defensible
Speed to insight
Signal to grounded answer in minutes, not hours.
Explainable drivers
Not just what changed — but why, and how it flows.
Defensible decisions
AI reasoning grounded in your actual cost context.
Scalable expertise
Analysis built on maintained knowledge, not one-off exports.
Getting started
You don't need a long implementation to see what contextual cost intelligence can do for your team. Your data stays yours; it isn't shared across customers and isn't used to train shared or general-purpose AI models.
01
Identify the right category
Pick a spend area with meaningful supplier complexity, commodity linkage, or recent cost movement. Direct materials, energy inputs, raw material contracts, and index-linked components tend to generate the strongest signal.
You walk away with
A scoped pilot category with a clear "what would good look like."
02
Build the context layer
MaterialX's AI-assisted processes structure your procurement, engineering, supplier, and market inputs — BOMs, CAD files, purchase orders, supplier contracts (including volume-discount and economic-adjustment agreements), commodity pricing, indices, and FX rates — into a live contextual intelligence layer for that category.
You walk away with
A driver map for the category, your team's formulas made explicit, a supplier-contract index, and a starting set of should-cost templates. AI assistants ready to interrogate the layer.
03
See what's there
A driver-level read on where costs are moving and why, a supplier-exposure map for the category, and a should-cost vs. paid-price gap analysis.
You walk away with
A prioritized list of negotiation, sourcing, or design-to-cost actions worth taking first.
A successful sprint becomes the template — expand across categories, teams, and suppliers. The contextual intelligence layer compounds over time.
Start harvesting AI now.