Case Study 01
Archaster Labs — founder, 0 to 1.
Archaster Labs · 2024 – present · Founder (2 co-founders)
Most decisions about land — what to source, where to build, what to restore — are made blind to the condition of the land itself. The data exists: satellites pass every five days, deforestation alerts are near-real-time, there's a forty-year record of surface water. But today the person who holds the decision hands their question to a spatial-data specialist and waits — and gets back a static answer weeks later, if the context survived the handoff. Archaster removes the handoff: the domain expert reads the landscape themselves, no GIS team in between. archasterlabs.earth
Archaster is a two-founder company — I'm CEO and CPO, my co-founder is CTO. I lead product: what it measures, how it reads a landscape, how it stays honest about what it knows. I work in a sandbox with real data and real interactions to figure out what's worth building, then it gets built. What follows is that product thinking.
- Company
- Archaster Labs (0 → 1) · 2024 – present
- Role
- CEO & CPO — product vision, strategy, design, and build in code with AI agents
- Product
- Nature intelligence platform for the domain experts who decide what gets sourced, built, and restored, without needing a geospatial background.
- Users
- Sourcing, design/materials, restoration and sustainability domain experts. Not spatial analysts.
- Core squad
- 2 co-founders · CTO builds, reviews and ships
- Status
- Live
Deciding what to see
Before any of this was designed, I spent roughly a year teaching myself satellite and geospatial science.
Enough to make the call that matters most: what should this product measure? Not "all available data." The specific sources, at the specific resolutions, mapped to the categories a sourcing lead actually decides on — deforestation pressure, water history, biodiversity by ecological function, protected and indigenous land. Every layer on the platform is one I chose and structured so a non-expert can act on it.
Screenshots of Archaster Labs software
The principles, written first
What not to do, written down before anything was built.
- Not only risk-based.
- Not over-simplified — simplicity is the goal, but over-simplification is a lie.
- Nothing that makes greenwashing easy.
- Old data shown as old, because time changes what a number means.
- And no "insight" that looks meaningful alone but misrepresents once you see its context.
The rest of this is what those principles look like on real ground.
Card from a Figma work file
Reading a landscape
Not just showing its data.
The product doesn't hand you data layers to interpret — it reads them together via an AI that labels how much it knows for the decision in front of you.
The AI analysis is based in the actual spatial data and always labeled by how far it sits from it:
I built the reasoning system on top of Gemini's and Anthropic's models: the frameworks it's grounded in, the system prompt, the role-based personas that answer as a procurement, sustainability, or materials lead rather than one voice for everyone. And the part that matters most in a domain where a confident wrong answer is dangerous — every claim is labelled by how it's known: observed, derived, inferred, or projected.
It leads with a TL;DR, and ends every analysis with three questions that would sharpen it, so the user sees what the data couldn't tell them on its own. It never rolls the signals into a single score.
Buffer zones
A site with two deforestation alerts inside its boundary and forty-two in the surrounding two kilometres is not the same as a site with none. One is stable. The other is intact today. The product reads which one you're looking at — because "this supplier is fine" and "this supplier is fine today" are different sentences.
Saying what the data can't
The hardest version of the honesty principle is biodiversity.
Species records are sparsest in exactly the richest tropical regions — so a data-poor map can look like a nature-poor place, the most dangerous misread in the product. It flags when records are low relative to what the ecoregion should hold — the difference between nothing lives here and no one has surveyed here.
Two ways in
Start from a place, or start from a material.
I'd always had a conviction that materials and place belong connected — that the people making sourcing and design decisions need to see a commodity and its landscape as one thing. I built the spatial side first: draw a location, get its ecological picture. But talking to companies surfaced the wall — most don't have the location data deep spatial analysis needs. That told me when to build the other half.
A commodity carries its own ecological signature: coffee depends on native bees, cocoa on midges; each crop has a water relationship and a set of ecosystems its cultivation threatens or depends on. So the product opens two ways — start from a place, or start from a material. Fusing them is what I'm working on now.
Down to the geometry
I tested multiple GIS tools and one thing they all had in common was that geometry errors were not shown and sometimes just silently failed an upload. So we built a user-friendly polygon uploader with immediate geometry checks, flagged in user-understandable language, and offering mitigation paths.