French startup Deepomatics has raised a $10.5 million (€10 million) Series B funding round. Although the founding round is relatively small, the startup has managed to convince some large clients to use its visual automation platform. Telecom companies, for example, use Deepomatic in the field to verify that tasks have been completed successfully.
EnBW New Ventures and Orbia Ventures are leading the newly announced funding round, which Deepomatic closed in October. Existing investors Alven, Hi-Inov Dentressangl and Swisscom Ventures are once again participating in a new round.
The startup has been around for a few years when I first covered Deepomatic in 2015. The company has always been focused on deep learning for computer vision applications. The main problem is that it has been a long journey to find the right customers for this technology.
With the telecom industry, it seems that Deepomatic has finally unlocked its true potential. “We discovered an industry that really needed what we were working on — and that was telecom companies,” co-founder and CEO Augustin Marty told me.
When a field worker installs fiber optic cables or rolls out a new 5G tower, they have to fill out complicated forms to make sure they’ve followed some specific processes. It can be quite annoying as employees can work for contracting companies. And those companies can work with multiple telecom companies with different requirements.
It’s also easy to make a mistake when filling out a form. Sometimes field workers can also say that something works well if it works a little. It can cause some QA issues, as we’ve seen with fiber concentration points.
That is why many field service companies work with photos. When they are done installing something, they have to take a picture of their installation and their instruments to prove that new equipment works with the right parameters. It means more work.
With Deepomatic, field service companies usually use photos as their benchmark. Photos are automatically analyzed to extract some knowledge from them. Deepomatic can then send some alerts if something is wrong and needs to be double checked.
“We started with the most complicated part, which is identifying errors,” said Marty. In addition, Deepomatic now sells an end-to-end platform so field workers only need to use Deepomatic to get things done. It also integrates with specific business tools such as ERPs.
When the startup works with a new client, there is some integration work so that Deepomatic works exactly as expected. It involves adding checkpoints, reusing some of the existing tasks in the computer vision library, or training the algorithm on a new set of images. Deepomatic algorithms are trained on the startup’s own infrastructure. But the product can run on the customer’s own cloud infrastructure and in some cases on-premises.
The company currently has about 20 major accounts, such as Bouygues Telecom, Swisscom and Movistar, as well as a number of smaller clients. Since this is a business software, customers usually pay hundreds of thousands of dollars per year to use Deepomatic.
Every month, Deepomatic monitors more than a million on-field operations. More than 20,000 field workers take photos with their phones every day and upload them to a Deepomatic backend.
Subsequently, Deepomatic and its team of 70 employees want to enter new markets and new industries such as renewable energy, electric mobility, construction, insurance, etc. Deepomatic wants to collaborate with companies in Europe, the US and South America.
Many governments and large companies are currently investing heavily to renew their infrastructure in the coming decades. At the same time, there is a shortage of talent for fieldworkers. It seems that Deepomatic is coming to the market at the right time to become an essential tool for this infrastructure overhaul.