Deepomatic wants to build the AI-based computer vision companion for field workers

French startup Deepomatic has raised a $10.5 million (€10 million) Series B funding round. While the founding round is relatively small, the startup has managed to convince some large-scale clients to use its visual automation platform. For instance, telecom companies use Deepomatic on the field to check 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 participating once again in a new round.

The startup has been around for a few years already as I first covered Deeepomatic back in 2015. The company has always been focused on deep learning for computer vision applications. The main issue is that it has been a long journey to find the right clients for this technology.

With the telecom industry, it seems like Deepomatic has finally unlocked its true potential. “We discovered an industry that very much needed what we were working on — and that was telecom companies,” co-founder and CEO Augustin Marty told me.

When a field worker is installing optical fiber cables or rolling out a new 5G tower, they have to fill out complicated forms to make sure that they followed some specific processes. It can be quite tedious as workers can be working for contractor companies. And those companies can be working with multiple telecom companies with different requirements.

It’s also easy to make a mistake when you are filling out a form. Sometimes, field workers can also say that something is working fine when it’s kind of working. It can create some QA issues, as we have seen in fiber concentration points.

That’s why many field service companies are also working with photos. When they are done installing something, they have to take a photo of their installation and their instruments proving that some new equipment is up and running with the right parameters. It means more work.

With Deepomatic, field service companies mostly use photos as their benchmark. Photos are automatically analyzed to extract some knowledge. Deepomatic can then send some alerts if something feels off and should be double-checked.

“We started with the most complicated part, which is identifying mistakes,” Marty said. On top of that, Deepomatic now sells an end-to-end platform so that field workers only have to use Deepomatic to get something done. It also integrates with specific enterprise tools like ERPs.

When the startup works with a new client, there is some integration work so that Deepomatic works exactly as expected. It involves adding control points, reusing some of the existing tasks in its computer vision library or training its algorithm on a new set of photos. Deepomatic algorithms are trained on the startup’s own infrastructure. But its product can run on the client’s own cloud infrastructure and in some cases on premise.

The company currently has around 20 large accounts, such as Bouygues Telecom, Swisscom and Movistar, as well as a bunch of smaller clients. As this is enterprise software, clients usually pay hundreds of thousands of euros per year to use Deepomatic.

Every month, Deepomatic monitors more than one million on-field operations. More than 20,000 field workers are taking photos with their phone and uploading them to a Deepomatic backend every day.

Up next, 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 work with companies in Europe, the U.S. and South America.

Many governments and big companies are currently investing heavily to overhaul their infrastructure for the next few decades. At the same time, there is a talent shortage for field workers. It seems like Deepomatic is arriving at the right time on the market to become an essential tool for this infrastructure overhaul.

Deepomatic wants to build the AI-based computer vision companion for field workers by Romain Dillet originally published on TechCrunch