We're bringing garment manufacturing back to American soil with robots, then using that same platform to sell directly to brands and to our own label — capturing the full margin of a $2 trillion industry, not just the sourcing fee.
A $2 trillion global industry running on labor that is increasingly expensive, unreliable, and far from home. CollectedAI is building the robots that replace that labor and using those robots to run its own clothing operations. Think of it like Tesla: instead of selling the technology to other car companies, Tesla built its own cars. We're doing the same with garment factories — proving the technology in Vietnam, then bringing automated factories home to the United States, where American-made, robotically produced clothing can undercut offshore competitors while CollectedAI keeps the entire margin.
The tech we're developing for fine-grained robot manipulation is among the most valuable in the world right now. No one else has a real-world data flywheel for it.
Our robot lab and small pilot factory. Engineers train robots on sewing tasks, iterate on designs, and produce initial brand samples. Closed loop: every failure teaches the system something. Brand lead Jan Nusbaum drives U.S. brand development and launch.
Our Da Nang facility — 35 staff, growing to 120 — runs live production while we deploy robots alongside human workers. High-volume, real-world data collection at scale. Revenue-generating from day one. Proof point for the larger raise.
Unedited footage and photos from New Jersey and Da Nang. Videos are sped up so the long clips read as quick loops — mute is on by default.
$800K raised from angels plus $180K from founders to date. This raise extends that same angel round.
Production data improves the robots; the robots improve manufacturing economics; B2B clients validate quality, cost, throughput, and reliability; the owned brand then turns the same manufacturing advantage into consumer upside.
CollectedAI's roadmap follows a staged industrial strategy: data, then robotic garment production, then U.S. manufacturing for established brands, then the owned brand, then full vertical expansion through additional factories, upstream assets, and a global rollout. We bring garment manufacturing closer to the American market at a cost structure that historically required Asia — then use that same platform to sell better-designed, better-priced products directly to consumers. This turns the upside of a U.S.-based CollectedAI into more than a patriotic story.
Before committing capital, CollectedAI and its advisors ran eight distinct directional decisions through the same evaluation framework: pain intensity, technical feasibility, deployment friction, buyer potential, ROI visibility, and expansion potential. This is that record — the full evaluation summary for each decision, unedited. Click any row to collapse it; the full 20+ page analysis is linked at the bottom.
Software licensing is the easier-looking path, but it is the weaker business model. The target market for robotic sewing software is weak due to limited R&D budgets, and incumbents face little urgency to adopt robotics when manual labor remains cost-effective. Internal deployment of the same software gives CollectedAI full control over hardware, workcell design, data feedback, and deployment quality, while opening a much larger commercial opportunity.
Owning production is more demanding, but it is where the real value sits. Leveraging robotic arms with our proprietary software is our competitive edge. It is capable of outworking humans due to its 24/7 robotic nature. It is more scalable, factories can be built more efficiently. Taking out the human hiring cost makes traditionally expensive areas suddenly viable again for manufacturing. These factories do not yet exist, CollectedAI will be the first of its kind.
Asia preserves the supply chain problems that make offshore sourcing painful, and Europe lacks a clear cost advantage while carrying restrictive regulations. The United States offers the strongest thesis: a massive consumption market, a documented appetite for nearshoring, and a tariff structure that makes domestic production financially compelling when robotics reduces the labor share. North Carolina emerged as the most practical starting location due to its existing textile workforce, mill infrastructure, and university research support. The trade-off is higher upfront capital against a superior landed-cost and working-capital case that offshore manufacturing cannot match under the current trade regime.
One single gigafactory would maximize standardization but scares customers as it concentrates operational, geographic, and capacity risk. Many scattered microfactories would improve flexibility but may struggle with unit economics, maintenance consistency, and real production scale. The stronger model is a controlled, modular manufacturing platform: start with one flagship U.S. campus with multiple medium to large factory units, then replicate standardized cells or medium-sized facilities as demand proves itself.
Retail margin is where the largest profit pool sits, and the cross-industry examples demonstrate that owning both production and the consumer relationship is viable when the economic and operational fundamentals are right. Robotic manufacturing is the wedge that makes this possible in apparel in a way that previous attempts could not sustain. Legacy companies like American Apparel failed because human labor at U.S. wages could not compete with Asian production costs. AI-driven robotics alters this equation by making U.S. manufacturing cost-competitive without tariffs and with faster turnaround. This shift validates the full vertical model as viable in a way that has never been possible before.
Manufacturing for established brands addresses a clear and urgent market need: brands face rising tariff costs, supply chain pressure, and a restructuring import landscape that creates immediate demand for a cost-competitive domestic alternative. Launching an owned brand from the start would add design, marketing, and consumer acquisition burdens before the manufacturing system is proven, while also requiring consumer education on the value proposition.
Serving established brands first builds production credibility, operating cash flow, and manufacturing capability, creating a foundation from which an owned brand can later be launched with lower risk. The economics of operating both a factory and a consumer brand are fundamentally different, and the added complexity of design, merchandising, inventory risk, and retail execution should not be taken on before the manufacturing system is proven. The careful path is to treat the owned brand as a later-stage opportunity, not the first step.
The optimal decision is to manufacture for other brands while developing and growing the owned brand. The market is not winner-take-all, and the strongest precedent supports a dual model: B2B production for utilization and credibility, owned brand for margin and strategic expansion.
The concept our Nusbaum dual team will pursue is mass-producing beautiful "thrift-store diamond" inspired designs from the 60s and 70s. Garments with the emotional appeal of rare vintage finds, but made available in modern sizing, consistent quality, and at substantially lower prices than boutique vintage-inspired brands. The concept has precedent in vintage-inspired brands and archive reissues, but the larger opportunity is to combine that aesthetic with a manufacturing system that can make rare-looking designs scalable.
CollectedAI should not launch the future brand as a traditional retail-first fashion company. The strongest rollout is DTC-first, but not DTC-only. Online should be the commercial core because it allows demand testing, waitlists, controlled drops, customer data collection, and fast feedback into the manufacturing system. Physical retail should come later, first through temporary pop-ups or fit showrooms, then through selective wholesale or flagship stores only once demand is proven. This creates a staged rollout that matches CollectedAI's advantage: test demand quickly, produce efficiently, replenish winners, and avoid locking the brand into expensive fixed retail before product-market fit is clear.
A staged, dual-channel model for robotic U.S. garment manufacturing and an owned consumer brand. Illustrative 5–10 year benchmark case, using public-company scale analogues rather than a top-down market-share assumption — and excluding internal manufacturing revenue for the owned brand.
Gokaldas and FIGS represent the two distinct revenue engines in CollectedAI's long-term model. Gokaldas is a modern, diversified apparel manufacturer whose product mix and operating complexity align with our medium-term capability roadmap. FIGS is a leading pure-play scrubs brand — a relevant proxy for the scale a focused, owned apparel brand can achieve while remaining modest relative to the broader fashion market. This is a conservative frame: manufacturing revenue is trending upward, inflation isn't priced in, and CollectedAI's robotics cost advantage is not reflected in either analogue's own economics.
Data engine + early revenue. Operate controlled Vietnam production to collect labeled workflow data while serving existing factory clients.
Prove robotics + unit economics. Validate cycle time, quality, uptime, labor reduction, and total landed cost through the NJ lab and NC factory environment.
Scale U.S. B2B manufacturing. Sell to established brands — scrubs first, then dresses, loungewear, and adjacent products as robotic cells expand.
Add the owned-brand layer. Use the same platform to capture retail revenue once manufacturing reliability, capacity, and demand are proven.
| Mature-scale case | Net margin | Annual net profit |
|---|---|---|
| Peer-like baseline (rounded) | ~5.0% | ~$53.7M |
| Internal automation upside* | 27.5% blended | ~$295.5M |
*Based on a 40% reduction in manufacturing COGS, with non-COGS expenses held constant — producing 36.0% manufacturing and 21.5% integrated-brand net margins.
| Year | Round | Range | Trigger |
|---|---|---|---|
| 2 | Series A | $20M–$30M | ~20 factory tasks automated |
| 3 | TBD | $45M–$50M | Established-brand production scaling |
Organized by topic and numbered to match how they're cited across our research. Where a source has a public link, it's linked directly.
Wanjie Textile (garment margin analysis) · Global Growth Insights (garment manufacturing market sizing) · Garment Manufacturing Market 2026–2035 report · McKinsey EU regulatory analysis · McKinsey (procurement & CPO priorities) · Compliance in Global Value Chains (working paper) · Swatch Group (company disclosures) · L'Oréal (company disclosures) · Urban Outfitters (recirculated garment figures) · Mandala (scrubs production cost analysis) · NurseMoneyTalk (scrubs brand sentiment) · Statista (retail & e-commerce data) · State of Apparel 2026 · Ecommerce Fashion in 2026 Trends · Insider Trends (flagship retail analysis) · JLL (U.S. retail market report) · Warby Parker (retail expansion case) · Inditex FY2025 annual results
Software licensing was the easy path and the wrong one. Production is harder, and it's where the value is. We start by manufacturing for established brands, where the tariff pain is real and the ROI is provable today. From that foundation, CollectedAI adds its own brand to capture the margin that currently belongs to retailers — one manufacturing platform, two demand channels, and a staged path to full vertical ownership.