Atollogy is revolutionizing how companies manage their manufacturing, ground and yard operations by integrating the physical world with artificial intelligence. Utilizing low-cost cameras and sensors, Atollogy monitors people, equipment and materials, and feeds that data into its machine learning software. Management by walking around (“MBWA”) is augmented by a digital twin of the operating environment, with alerts that empower management to act. The result is improved efficiency for customers, which in turn reduces waste in energy, materials and human capital. By focusing on the customer journey and delivering management the information they need, when they need it, Atollogy is raising the bar for enterprise software.


Q & A with Atollogy

What motivated you to found your company?

During my 25 years in the enterprise software industry, I frequently thought there HAD to be a way to make the whole process of selecting, buying, and implementing enterprise solutions simple for business. Seeing the trends in low-cost IoT devices and the availability of cost-effective machine learning emerge over the last several years, I saw an opportunity. What if we applied these technologies to physical operations challenges—like manufacturing and logistics—that could not be addressed in any reasonable form with “classic” software? I decided it was time to finally try my hand at building a company in a space I am passionate about, with a very customer journey-focused approach to introducing sophisticated software to a generally non-technical audience. Atollogy is re-inventing how companies manage their physical operations AND what they should expect from an enterprise technology provider.

Why did you choose to partner with Valo?

From our first meeting with the team at Valo, it was clear they would be a great partner for our growth. The breadth and nature of their experiences are very relevant for Atollogy, including experience with machine vision based solutions. Most importantly, their outlook is aligned well with ours – we are applying machine learning and artificial intelligence at scale in a democratic fashion to help companies and people be better. The fact that they not only got where we’re going, but were also so excited about it, was a huge factor for us.