Stch Raises $7M to Expand AI-Driven Fabric Manufacturing

CryptoFrontier

Bengaluru textile startup Stch has raised US$7 million in a pre-series A funding round led by Omnivore, with participation from Kae Capital and WVC, according to YourStory. The company plans to use the funds to expand its AI tools and partnerships with mills and fashion brands.

Company Background and Founders

Stch was founded in 2025 by Narahari Payala and Aseem Chitkara, both former executives at Zetwerk. The startup operates a contract development and manufacturing model for fabric research and production across India and other Asian regions.

AI-Driven Fabric Reverse-Engineering

Stch uses artificial intelligence to reverse-engineer fabrics for manufacturing rather than creating consumer-facing design tools. The platform reads images and descriptions, then converts them into technical specifications such as texture, weight, and finish. Once specifications are generated, the startup recreates those materials in partnership with local manufacturers.

The approach has demonstrated cost benefits. According to the company, one UK brand lowered sourcing costs by nearly 20% after using Stch to reproduce its fabrics in India instead of Turkey.

Business Model and Manufacturing Network

Stch does not own factories. Instead, it secures production capacity from partner facilities and manufactures through a network across India and Bangladesh. This asset-light model allows the company to scale production without capital-intensive infrastructure investments.

Market Traction and Competitive Positioning

The company has an order book exceeding US$15 million from brands in the UK, Europe, and the US. Stch is building a proprietary set of “fabric recipes” based on accumulated data about fabric development and manufacturing knowledge, which the company views as a defensible competitive advantage.

The startup represents a broader trend in fashion technology where AI value is generated through factory and supply chain operations rather than consumer-facing tools. This approach addresses growing geopolitical risk in manufacturing by enabling brands to diversify production across multiple regions and reduce concentration risk in their manufacturing base.

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MarginMothvip
· 22h ago
Their "fabric recipes" library is growing larger and larger, and it is expected to form a moat.
View OriginalReply0
DaoSideQuestvip
· 04-25 10:09
Asset-light, expanding through partner factories, the supply chain combination of India + Bangladesh is very clever.
View OriginalReply0
RugPullEnjoyervip
· 04-23 20:57
If reverse engineering is done reliably, the sampling cycle and communication costs can be greatly reduced.
View OriginalReply0
SushiLatencyvip
· 04-23 07:41
7M Pre-A dares to push for global brand orders, proving that the product can truly be implemented, not just a PowerPoint presentation.
View OriginalReply0
GateUser-08ae47f3vip
· 04-23 02:22
If sample, color matching, ingredients, and weight can be generated with one click, the procurement team would probably love it to death.
View OriginalReply0
GateUser-991fc58avip
· 04-23 02:14
I hope it doesn't become just a tool for "exploiting contract manufacturers." It would be even better if it could also optimize process transparency and profit sharing.
View OriginalReply0
GateUser-8d51653bvip
· 04-23 02:09
It also depends on the data source and error control. To what extent can fabric feel and elasticity be accurately reproduced relying solely on images?
View OriginalReply0
HoldingPositionsIsLikeTendingvip
· 04-23 02:07
Diversification of geopolitical risks is very practical; the fashion supply chain has been educated too many times in recent years.
View OriginalReply0
GateUser-3f3455c7vip
· 04-23 02:07
Standardized specification output is like opening up the black box on the factory side; it's quite imaginative.
View OriginalReply0
GateUser-c1cab702vip
· 04-23 02:07
The 15M+ order book is just too strong. Are European, American, and British brands now adopting this AI + manufacturing approach?
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