French startup Deepomatic has raised $10.5 million (€10 million) in Series B funding. Although the startup round is relatively small, the startup has managed to convince some large customers to use its visual automation platform. For example, telecom companies use Deepomatic in the field to ensure that tasks are completed successfully.
NBW 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 will also participate in the new round.
The startup has been around for a few years, having first covered Deepomatic in 2015. The company has always focused on deep learning for computer vision applications. The main issue is the long journey to find the right customers for this technology.
With the telecom industry, Deepomatic seems to have finally unlocked its true potential. “We found an industry that really wanted what we were doing — and that was telecom companies,” said founder and CEO Augustin Marti.
When a field worker installs optical fiber cables or deploys a new 5G tower, they must fill out complex forms to ensure certain procedures are followed. It can be very boring because employees work for contractor companies. And those companies may work with multiple telecom companies with different requirements.
It’s easy to make a mistake when filling out a form. Sometimes field workers can also say that something is working well when it is working. It can cause some QA issues as we have seen with fiber concentration points.
This is why many field service companies are working with photos. When they finish installing something, they have to take a picture of the installation and their equipment ensures that some new equipment is working with the correct parameters. It means more work.
At Deepomatic, field service companies mostly use photos as benchmarks. Photos are automatically analyzed to extract some knowledge. Deepomatic can then send some alerts if something is heard and it needs to be double checked.
“We started with the most complicated part, identifying the errors,” said Marty. On top of that, Deepomatic now sells an end-to-end platform so field workers only need to use Deepomatic to do something. It also integrates with specialized enterprise tools such as ERP.
When the startup works with a new client, there is some integration work for Deepomatic to work properly. It involves adding control points, reusing some functions in the computer vision library or training the algorithm on a new set of photos. Deep algorithms are trained on the startup’s own infrastructure. But the product can be run on the customer’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 many smaller clients. As this is enterprise software, customers pay hundreds of thousands of euros per year to use Deepomatic.
Each month, Deepomatic tracks more than one million field operations. Over 20,000 field workers are taking pictures on their phones and uploading them to the Deepomatic backend every day.
In the future, Deepomatic and a 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, America and South America.
Many governments and large companies are currently investing heavily in infrastructure maintenance for the next few decades. At the same time, there is a shortage of field workers. Deepomatic seems to be coming on the market at the right time to be an essential tool for this infrastructure upgrade.
