Artiso banks $3.4m pre-seed to build AI workflow layer for fashion supply chains

Fashion software startup Artiso has raised just over $3.4m in pre-seed funding to scale an AI workflow platform that, it claims, produces a complete tech pack (the specification document a brand sends a factory) in about four minutes, against two to six hours through existing processes.

Criteria Venture Tech led the round. Swanlaab Venture Factory and Bynd Venture Capital participated, alongside angel investors David Tomás, co-founder of digital marketing firm Cyberclick, and Nicolaj Reffstrup, founder and former chief executive of Danish brand GANNI, investing through Look Up Ventures.

What does the platform actually replace?

Founded in July 2025, Artiso targets a familiar pain point for design and production teams: workflows scattered across separate product lifecycle management (PLM) systems, design software, image-generation tools, shared drives, spreadsheets and email. The platform consolidates those steps and deploys AI agents across the full chain — from inspiration and moodboards through design, product development and marketing.

Its toolset covers the concrete deliverables that sit between a design sketch and a production order:

• flat-sketch development
• tech-pack creation
• virtual models
• on-model imagery
• packshots
• campaign assets

The software also carries a brand archive holding information on previous products, bestsellers, seasonal direction and visual identity. Artiso uses that data to tailor outputs to individual brands, and gives creative, product development, merchandising and marketing teams access to the same system — effectively collapsing the hand-offs that usually lose information between departments.

"I spent years leading creative teams that had to orchestrate every step of a collection across tools that never spoke to each other," said Artiso co-founder and Chief Executive Officer Sarah Iglesias Hiller.

"So we built the operating system we always needed: one that knows each brand's archive, its customers and its taste, and that is already running inside fashion companies today. AI is changing how every collection is imagined, made and shown, and with Artiso, creativity, not complexity, defines fashion's next era."

Where is the money going?

Artiso said the funding will support further development of its operating system, team expansion and enterprise roll-outs. Hiring plans span both fashion and technology roles, including:

• garment technology
• pattern making
• design
• art direction
• motion
• data science

That hiring mix signals where the company thinks the product is heading: deeper into pre-production technical territory — pattern making and garment technology sit squarely in the tier between design and factory — rather than staying a marketing-asset generator.

Which markets does this touch?

Artiso already works with mid-sized and enterprise fashion brands, retailers and manufacturers across Europe, North America and Asia. It has identified the US as a priority market as it shifts from initial development toward broader commercial deployment.

The four-minute tech-pack claim is the number that matters here. Tech packs are the contractual backbone of any sourcing relationship — they carry measurements, materials, construction details and tolerances that factories price and produce against. If an AI platform can generate them accurately in minutes rather than hours, it compresses the pre-production cycle for brands and, in theory, shortens time from design sign-off to factory quotation.

The caveat is the word "accurately". The claim comes from Artiso itself, not from an independent benchmark or a named customer, and the round is a pre-seed — the earliest institutional stage. Buyers should treat the speed figure as vendor-reported until enterprise roll-outs produce reference customers.

For sourcing and product development teams evaluating workflow consolidation, the decision this news forces is straightforward: if tech-pack generation and archive-driven design iteration can be unified in one system, the question is no longer whether to digitise pre-production documentation, but whether a four-month-old startup can deliver enterprise-grade reliability before the established PLM vendors bolt the same AI onto their installed bases.