What is Pachama?

Pachama is a climate technology company that uses artificial intelligence and satellite data to monitor forests and verify carbon credits. Its platform helps companies, project developers, and investors understand whether forest-based carbon projects are real, measurable, and deliver long-term climate impact.

Instead of relying only on manual field surveys, Pachama combines remote sensing, machine learning, and ecological science to assess forest health, carbon storage, and risk at scale.

Originally founded in 2018, Pachama became known for bringing data-backed measurement and verification to forest carbon projects, reducing reliance on infrequent field surveys and improving transparency in the voluntary carbon market.

Pachama is an AI-based forest monitoring and verification platform that supports reforestation and forest conservation projects by measuring carbon impact and forest health over time.

Purpose of Pachama’s Software and Website

The main purpose of Pachama is to bring trust and transparency to the voluntary carbon market. Forest carbon projects play a major role in climate mitigation, but they are often hard to verify and monitor over time.

Pachama’s software helps:

• Buyers evaluate the quality of carbon credits

• Project developers measure and report forest carbon more accurately

• Organizations track forest change and carbon impact over the years, not just at one point in time

Its website acts as both a platform for carbon project analysis and a marketplace connection between verified projects and buyers.

Core Features of Pachama

• AI-Powered Forest Monitoring: Integrates satellite imagery and machine learning to assess forest cover and carbon density over large areas.

• Carbon Reporting and Verification: Generates measurable estimates of carbon stored and changes over time.

• Risk Detection: Identifies disturbances such as deforestation or degradation that could reduce a project’s carbon value.

• Project Quality Insights: Offers data to help buyers choose high-integrity carbon credits.

• Portfolio Tracking: Lets organizations monitor multiple projects in a central dashboard.

Benefits of Using Pachama

• Improves confidence in forest carbon credits

• Reduces reliance on slow and costly manual field measurements

• Enables ongoing monitoring instead of one-time verification

• Helps companies make more informed climate investment decisions

• Supports transparency and accountability in carbon markets

For organizations concerned about greenwashing, this level of data-backed insight is especially valuable.

What Makes Pachama Special

What sets Pachama apart is its focus on continuous, technology-driven verification. Traditional forest carbon monitoring often relies on periodic field audits that can miss changes between assessments.

Pachama’s approach:

• Uses up-to-date satellite data

• Applies AI models trained specifically on forest ecosystems

• Scales across regions and project sizes

• Focuses on long-term forest outcomes, not just credit issuance

This makes it particularly useful in a market where trust and data quality matter more than volume.

Acquisition by Carbon Direct

In November 2025, Pachama was acquired by Carbon Direct, a science-driven carbon management company. The acquisition aims to combine Pachama’s digital monitoring, reporting, and verification technology with Carbon Direct’s deep scientific expertise and advisory services. This integration enhances the combined company’s ability to deliver high-quality carbon measurement and verification tools while supporting broader climate goals.

Conclusion

Pachama addresses one of the biggest challenges in climate action: knowing whether forest carbon projects truly deliver the impact they promise. Combining AI, satellite monitoring, and forest science brings greater transparency and accountability to the carbon market.

For companies and project developers looking to support credible, high-quality forest carbon solutions, Pachama provides the tools needed to measure, monitor, and trust climate impact over time.