Grand Rapids Firm Used AI to Recover Materials

Woodchuck launched an innovative process to turn construction waste into biomass fuel.

Updated on Oct. 12, 2026 in Manufacturing

Grand Rapids Firm Used AI to Recover Materials

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Is it better to prioritize solving specific problems over adopting new technologies like AI?

Grand Rapids-based company Woodchuck has developed an AI-driven method to identify and recover construction materials for biomass energy production. The system processes wood waste from construction sites to be ground for electricity generation.

Why it matters

The business model seeks to lower hauling costs by maximizing the volume of material transported in each load, effectively increasing the efficiency of construction waste management.

Woodchuck utilizes 10 camera-equipped dumpsters to train AI systems that capture imagery 24-7. This approach increases load capacity to 25 to 30 tons per semi-truck, an efficiency gain of 2 tons per haul.

The players

Thomas

He is the founder of Woodchuck and the author of the book Starving for Innovation.

Woodchuck

This Grand Rapids-based company utilizes AI image-recognition technology to process construction waste into biomass fuel.

Ryan Cos.

This construction firm collaborated on a project where Woodchuck encountered difficulty identifying materials due to snowfall.

Stanley Black & Decker

This global power tool manufacturer is where the Woodchuck founder previously worked on a connected drill prototype.

The details

The firm uses AI image-recognition to sort construction materials on-site before consolidating them for grinding into biomass fuel. Founder Thomas, a former Stanley Black & Decker employee, launched the venture after a previous prototype drill project failed to reach the market.

Woodchuck launched its first project in January.
Thomas participated in an interview with ENR on October 9, 2026.
An article regarding the book Starving for Innovation was published on October 12, 2026.

Market Landscape

Woodchuck is applying AI image-recognition software in industrial waste management to disrupt traditional construction site hauling. This approach marks a departure from manual sorting methods by automating material identification to increase density and reduce transportation costs.

Local construction sites in Grand Rapids may see changes in how waste is managed and hauled as these AI-equipped dumpsters become more common. These adjustments aim to reduce overall project hauling costs, potentially impacting disposal pricing for local contractors.

The takeaway

The firm's success depends on the reliability of AI systems, as seen when weather conditions like snowfall interrupted material identification on a recent project. Companies adopting such technology must account for environmental variables that can disrupt automated sorting processes.