Material switch recommendation
Material switch recommendations: purchase items classified into the category taxonomy with the AI's guess and reasoning, validated or corrected by the buyer, natural-language rules that encode buyer knowledge, a chat that answers questions about a material from its drawing, and alternative materials proposed on cost, compatibility and vendor performance.
One working session with the people in the seat: which decisions, which systems, what a good week looks like.
The app you see above, running on your own exports instead of the simulated world. Every screen, every decision, your numbers.
Wired to your systems with your permissions, agents proposing, people approving, the ledger recording who did what.
Overview
The same bolt is bought under a dozen descriptions from four vendors. When one runs short nobody knows which of the others fits, and the buyer starts from the drawing.
The app harmonises material descriptions from text, PDFs and images into one taxonomy, with the reasoning shown and the buyer's validation captured. Natural-language rules encode what a buyer knows about equivalence. When production needs a substitute, the alternatives are ranked on cost, compatibility and vendor performance and the buyer can ask questions before switching.
Key features
- Encoded specialized buyer knowledge: natural-language rules that identify similar materials automatically.
- AI-powered material reconciliation: schematics and drawings read with OCR and vision models to extract features and find substitutions.
- Buyer actions prioritization: purchasing strategies on cost and inventory, such as price differences across suppliers or using existing stock.
- Every classification carries its reasoning and the buyer's validation.
Decisions it produces
- Validate or correct a classification
- Write a rule for similar materials
- Ask a question about a material
- Accept an alternative material
Data sources
| Material master | items, descriptions, vendors |
|---|---|
| Drawings | PDFs and images |
| Purchase history | prices and lead times per vendor |
| Vendor performance | quality and delivery scores |
Simulated data throughout. Book a call to see it on your own systems.
Related demos
Material harmonization
Raw materials from several ERPs enriched with properties extracted from safety data sheets and certificates of analysis, grouped into harmonized materials with AI suggestions, so a bill of materials can name an alternative supplier.
Automated procurement sourcing
An inbox for purchase requests that arrive as emails, PDFs and ERP entries: each is parsed into material, quantity and need date, matched to the material master, priced across vendors on unit price and lead time, and proposed as a purchase order with a confidence score and a written reason for the buyer to approve, edit or assign.
Supplier management
A supplier 360 with the open purchase orders and shortages that need attention, per-supplier performance with on-time history and risk, and a risk categories page where the rules for supplier risk, order risk and human review are written in plain language and applied across the book.