What it sells
Industrial AI.
Business model breakdown
Buyer: who owns the budget?
UnverifiedEngineering/R&D, production and aftersales leaders at OEMs and suppliers (automotive, defense and aerospace, industrial machinery, railway).
Trigger: what makes them buy now?
Complex products run on engineering data that does not connect (PLM, ALM, ERP, CAD), so errors are found late; company pitches shifting error detection left.
Workflow: what inputs become what output?
Connect sources such as Teamcenter, DOORS, Jira, SAP and CAD into one product ontology, consumed by apps (Product Explorer, Requirements Manager, Error Inspector, Ticket Analyzer) and AI agents like Claude Code.
Economics: what it saves and how it charges
Enterprise deployments per customer (19 engagements listed, live and pilot); claimed impact: EUR 20M+ per year for a European automotive group, errors caught at architecture review cost ~1,000x less than in the field.
Distribution: how it reaches customers
Enterprise 'Get started' / contact-led.
Funding history and coverage
| Round date | Stage | Amount | Investors | Source |
|---|---|---|---|---|
| 2026-04-29 | — | €25M | — | news.google.com |
Profiles summarize public reporting and company websites. Business-model notes are MaxBC's reading of that material, not confirmed facts; unknowns are left blank rather than guessed. Updated 2026-10-12.