Built for projects that matter—anywhere on our blue planet.

Messy handwriting in the field, then hours spent re-entering data into spreadsheets—doubling the time and risk of mistakes.

Using R and GIS can easily eat up hundreds to thousands of hours.

Data analysis reveals what’s happening and what’s needed—but most data goes unused, underused, or delayed. That’s wasted effort, costing time, money, credibility, and smarter decisions.

Nothing is ‘free’. A lack of research-grade ethics, especially in data, pose risks to species, places, or people if they are selling data or making the data open access. *Imagine a free nature-logging app that exposed or sold the exact sites of sharks, pangolin, or abalone—they’d be gone pretty quick.

If you don’t have in-house data science experts, expect to spend $60,000–$150,000+ per project—and still start from scratch next time. Efficiency is key.

Just one data scientist can take 8 months to execute even a small data project—at $100/hour, that’s $140,000. Shouldn’t their time be spent on important tasks, not on basics like data cleaning and exploratory analysis?

Many organizations still use phone hotlines for public reports—animals, injured animals, pollution, illegal activities, invasive species, and more—but the experience is frustrating, leading to apathy, underused data, and growing conflict.

Decision making—without analysed data—means you are left guessing. What’s the cost of getting it wrong?