Built in Bandung because that is where the problem is
SPUN is an AI supply-chain visibility platform for fashion brands sourcing from Indonesian garment factories. We are based in Bandung, which is one of the largest garment production clusters in Southeast Asia.
Why we built this
Indonesian garment factories supply a large share of the global mid-market fashion output. The production quality is competitive. The unit economics work. The gap is not product. The gap is information.
Fashion brands managing 20 to 100 active purchase orders at a time have no structured way to know where each order stands in production. They follow up by WhatsApp. Factories give verbal assurances. A delay surfaces the week of the expected ship date when there are no good options left.
We found that the delay problem is not usually about bad faith. Brands and factories often lack a shared view of what information is needed, when it is needed, and how a change in production should be communicated. That gap is structural: factories manage shifting production pressure while brands plan around incomplete updates. Both sides lose value when milestones are not visible early enough to act.
SPUN is a structured bridge. Brands get milestone visibility and delay risk scoring. Factories get a clear order format and a simple checkpoint reporting channel. Neither side has to change how their business operates to make it work.
Where we are now
We are in the early stage of building, with a working platform and a small initial cohort of brands and factories participating in a paid pilot program. Our focus is Bandung and the surrounding West Java production corridor. As we validate the model, we will grow to additional production regions in Indonesia.
We are not backed by institutional capital. We are building with the revenue generated by the brands that use the platform. That keeps us focused on whether the product is actually solving the problem, not on growth metrics.
People who have worked in the garment trade
Apparel merchandising and sourcing across South and Southeast Asia are Monesh Lalwani's core domain, with more than six years working on multi-factory order runs from initial specification through final shipment. He has firsthand experience with the communication gaps between fashion brands and factories, and how those gaps create preventable delays.
West Java garment factory operations and production capacity planning are Amara Silitonga's core areas, shaped by five years working with factory schedules and production milestones. She has coordinated between Bandung-area textile manufacturers and export-market buyers, with particular focus on tracking deviations and keeping production information aligned within factory scheduling systems.
Data pipelines and operational event-routing systems are Rizky Purnama's technical focus. He has built systems at logistics-adjacent companies and worked with supply chain message audit trails, structured event logging, and order lifecycle tracking at transaction volume. That background informs SPUN's approach to making production events traceable and useful for operational decisions.
What we believe about building this product
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Specific information beats general reassurance
A delay alert that says "sew complete logged four days past committed date" is useful. An alert that says "your order may be delayed" is not. We build toward specificity.
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Both sides of the trade need to work
A platform that helps brands at the expense of factories will not survive. Factories need to get clearer orders and fewer interruptions from using SPUN, or they will not participate. We design with both sides as the end user.
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Integration complexity is a failure mode
If a factory needs an IT consultant to participate in SPUN, most factories will not participate. If a brand needs to retrain their sourcing team, most brands will not switch. We stay with the minimum setup that actually works in a factory environment.
We are building a small team
We occasionally bring on people with direct experience in garment sourcing, production operations, or supply-chain data engineering. We are not posting roles publicly at this stage. If you have relevant background and want to talk, email us with what you have done.