DeepSnow — The Polymer Platform Behind Better Snow
A marketing site and enterprise platform for an AI-driven snowmaking chemistry venture — from pilot inquiry through deployment, monitoring, and ROI modeling.
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DeepSnow is a deep-tech venture with a deceptively simple pitch — “the polymer platform behind better snow.” Behind it sits a wet lab, an AI discovery engine, and a lead product, SL6733, that ski resorts dose into water they already pump. We built the surfaces that turn that science into something an operator can understand, trust, and buy.
The challenge
Selling polymer chemistry to ski-resort operators is a hard communication problem. The value is real — a roughly +3°C wet-bulb advantage and hundreds of extra snowmaking hours a season — but it lives in molecular-weight curves that don’t sell themselves. SL6733 pairs an ultra-high-molecular-weight acrylamide copolymer that inhibits ice recrystallization with a cold-water-swelling starch nucleator, and it has to read as credible and EU-compliant — a biodegradable alternative to Snomax, which is restricted in France, Austria and Bavaria — rather than experimental. One audience wants the science; the other wants the season economics.
The approach
We built the marketing site on Next.js and Astro for speed and clarity, leading with the product story rather than a brochure download. The operating platform runs on React and TypeScript over a Django and PostgreSQL backend, carrying a resort from pilot request through deployment and into season-long monitoring. Compliance language and the financial framing live in the same system, so the numbers an operator sees on the site are the numbers the platform stands behind.
What we built
- A live ROI model that returns an operator-specific projection in about 30 seconds with an email-me-the-result CTA, instead of a static brochure
- Product pages explaining how SL6733 works — Volume Mode (~50% more snow per shift) versus Economy Mode (~50% less water and energy), at a 6–7.6 ppm drop-in dose
- A three-layer platform narrative: the in-house Wet Lab, the AI Discovery Engine that ranks polymer candidates by ice-binding affinity, and the Product Pipeline (SL6733, plus DS-100 and DS-400 in R&D)
- A pilot enrollment flow for the 2026/27 EU lab program with limited-slot framing
- Operator-facing monitoring views tracking snow quality and season economics through deployment
The outcome
DeepSnow is the kind of work this studio likes most — a genuinely technical product made legible without dumbing it down. The same care goes into a marketing site or a conversation about a build; only the subject matter changes.