♻️ BinIt: A Cutting-Edge AI Platform For Recycling
With a best-in-class computer vision system, BinIt helps waste processors accurately sort through recyclable and non-recyclable materials.
In a world that generates over 2 billion tons of municipal solid waste every year, our ability to adapt to increasing amounts of waste depends on whether we can effectively recycle and sort it. This week’s startup has a way to help waste management facilities meet their full potential.
🔥 What’s the Deal?
Over 90% of all plastic produced is never recycled, which is a key statistic that shows how the $1.6 trillion recycling and waste management industry is curiously archaic and in dire need of support. Transportation, sorting, and disposal all rely on age-old approaches which are ripe for reinvention. Recent Columbia Engineering grads Raghav Mechri and James Bollas are targeting recycling inefficiency with their elegant analytics platform, BinIt.
After recyclable materials are picked up by hauling trucks, they are transported to large industrial sorting centers called Material Recovery Facilities (MRFs). These facilities have to sort mixed recyclables into different material categories. However, these sorting practices are often terribly ineffective; in fact, the oldest MRFs still rely heavily on manual sorting, which is not only an inefficient and expensive process but was hard to implement during the heights of the pandemic due to concerns over social distancing.
To better automate MRFs, BinIt introduced WasteClassify, a proprietary computer vision system that gives recycling facilities detailed, real-time data on their sorting systems and the business implications. The system is easily installed above a facility’s conveyer belt and can accurately classify and tag different materials, detect incorrectly sorted items, and even mark potentially dangerous contaminants. This improved sorting system can have a significant environmental impact: BinIt estimates that if their WasteClassify system was installed in every MRF across America, an astounding 100 million tons of waste would be diverted from landfills.
🚀 Fast Facts
Company: BinIt
Website: binit.ai
Founded: 2019
Stage: Seed
Industry: Waste Management
SDG: #12: Sustainable Consumption and Production
Team: Raghav Mecheri and James Bollas
Traction: Successful pre-seed round led by Neotribe Ventures, winner of the 2019 Columbia Venture Competition ($25K in funding)
❤️ Why We Love It
🔑 Addressing a key sustainability need
Recycling is crucial to a sustainable future, as constant production drains resources and creates pollutants. The case for automated MRFs is clear: they are far more efficient and reduce the need for significant amounts of labor. BinIt’s WasteClassify system can fulfill an MRF’s need for an advanced optical classification system and comes with a full suite of features that few competitors have implemented. These include an ability to identify dangerous contaminants that are common at recycling facilities and alert human workers before they are exposed to them. WasteClassify can also assist in pricing analysis, being able to estimate and aggregate the relative market prices of the materials it classifies.
By improving both the environmental and business case of recycling, Binit fulfills a crucial role in bringing us closer to a circular economy. The platform also importantly encourages more MRFs to open - a dire need as several MRFs around the country shut down during the pandemic.
✅ It’s tried and true
The BinIt system has already been installed in MRFs to great success. On average, BinIt diverts an additional 25% of waste away from landfills. When it comes to identifying types of waste, their proprietary algorithm has an accuracy rate of 95%, making it one of the most accurate computer vision implementations in the world. BinIt is an incredibly practical investment for a recycling facility, making it a sure business bet.
🌎 A commitment to impact
From the beginning, Raghav and James have been laser-focused on designing a product that genuinely solves an issue. Originally, when they won the Columbia Engineering Design Challenge and the Columbia Venture Competition, they had proposed a robotic garbage bin that would automatically sort waste. Realizing that it was too cumbersome and expensive, they iterated through concepts before arriving at the idea for an analytics startup. As it has no negative impacts on the environment or industry stakeholders, BinIt is the perfect example of sustainable innovation. Raghav and James hope to expand BinIt’s analytics offerings to other stakeholders to further improve the waste management system, paving a greener path for landfill owners, waste haulers, and consumers.
👋 Get Involved
Inquiries? Shoot them an email (raghav@binit.ai)
BinIt is hiring across sales, product, and engineering! Read more here.
—Yash Mangalick (Columbia)