Soil analysis costs kept global mapping out of reach. Migrating to Earth Engine drove 10x faster AI iteration and doubled revenue.
A climate technology company analyzing global soil health to sequester carbon, processing terabytes of geospatial data and soil samples to verify regenerative agriculture practices.
Traditional methods for measuring and verifying soil carbon face high costs and scaling limitations, hindering the ability to identify promising...
“I was working in an atmosphere lab at NOAA, analyzing air samples when we crossed the threshold of 400 parts per million of carbon dioxide in the atmosphere. I knew then that I wanted to help find a way to heal the planet. Humans generate 40 to 50 gigatons of carbon emissions annually. There are few things that could even theoretically make a dent in that. I couldn’t find a climate solution that was both fast and scalable that didn’t come with bad unintended consequences.”
Perennial's Digital soil mapping is part of this use case:
MMRV platform for field-level soil carbon sequestration and emissions monitoring.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Assisted Perennial with migrating terabytes of data and developed new predictive model features.
Related implementations across industries and use cases
Field sensors were costly and prone to theft. Vertex AI analyzes satellite radar to verify drainage with 99.6% accuracy.
Assets relied on annual physical checks. Automated agents now scan images hourly, detecting leaks and heat anomalies instantly.
Brands struggled to catch fleeting trends. Now, AI search digests tens of millions of daily data points for millisecond results.
Engineers spent weeks manually configuring infrastructure. Now, they deploy pre-optimized models in minutes.
Engineers spent 90% of time on data prep. New pipelines flipped that to 90% modeling and cut tuning from 7 days to 1 hour.
Routine requests buried support staff. AI agents now resolve 71% of cases autonomously, from billing answers to smart charging setup.
Manual call notes burdened agents, while managers struggled to track promises. Now, AI copilots automate summaries and verify commitments.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Soil analysis costs kept global mapping out of reach. Migrating to Earth Engine drove 10x faster AI iteration and doubled revenue.
A climate technology company analyzing global soil health to sequester carbon, processing terabytes of geospatial data and soil samples to verify regenerative agriculture practices.
Traditional methods for measuring and verifying soil carbon face high costs and scaling limitations, hindering the ability to identify promising...
“I was working in an atmosphere lab at NOAA, analyzing air samples when we crossed the threshold of 400 parts per million of carbon dioxide in the atmosphere. I knew then that I wanted to help find a way to heal the planet. Humans generate 40 to 50 gigatons of carbon emissions annually. There are few things that could even theoretically make a dent in that. I couldn’t find a climate solution that was both fast and scalable that didn’t come with bad unintended consequences.”
Perennial's Digital soil mapping is part of this use case:
MMRV platform for field-level soil carbon sequestration and emissions monitoring.
Cloud computing services, AI infrastructure, and data analytics platforms for enterprises.
Assisted Perennial with migrating terabytes of data and developed new predictive model features.
Related implementations across industries and use cases
Field sensors were costly and prone to theft. Vertex AI analyzes satellite radar to verify drainage with 99.6% accuracy.
Assets relied on annual physical checks. Automated agents now scan images hourly, detecting leaks and heat anomalies instantly.
Brands struggled to catch fleeting trends. Now, AI search digests tens of millions of daily data points for millisecond results.
Engineers spent weeks manually configuring infrastructure. Now, they deploy pre-optimized models in minutes.
Engineers spent 90% of time on data prep. New pipelines flipped that to 90% modeling and cut tuning from 7 days to 1 hour.
Routine requests buried support staff. AI agents now resolve 71% of cases autonomously, from billing answers to smart charging setup.
Manual call notes burdened agents, while managers struggled to track promises. Now, AI copilots automate summaries and verify commitments.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.