Making Artificial intelligence Ideas into ACTION


We aim for a good fit in every project, we are interested in developing long term relationships with our clients and only narrow in computer vision and machine learning related problems in order to serve best and add the most value for them. We are based in Montevideo, Uruguay with US friendly time-zone.

Even though we as a company have only 10 months of existence, our engineers have 7+ years of experience in computer science and we already achieve to start working for a couple of well-founded startups from all around the world.

We are just getting started and we are eager to bring Artificial Intelligence to more companies and entrepreneurs around the world.

$50 - $99 / hr
2 - 9
Show all +
Montevideo, Uruguay
  • Andes 1365 Esquina 18 de Julio ap 420
    Montevideo, MO 11600


Key clients: 

Flyfut SL, Medicalhub, Cyboard, Sensing Places LLC


Sort by

Machine Learning Development for Video Production

"The code is commented perfectly. They track everything they do, and they’re proactive in proposing things."

Willing to refer: 
The Project
$50,000 to $199,999
Feb 2020 - Ongoing
Project summary: 

A company that records soccer matches with drones hired Machine Learning to develop a machine learning model to pre-identify the events of a match so that editors didn't have to watch the entire thing.

The Reviewer
1-10 Employees
Alvaro Ybanez
CPO, Sports Media Company
The Review
Feedback summary: 

The efficiency of the video editing process has improved five-fold, taking one-fifth the amount of time to edit a match. The team at is communicative, perhaps even overly so, as well as being incredibly transparent, proactive, and helpful.

A Clutch analyst personally interviewed this client over the phone. Below is an edited transcript.


Introduce your business and what you do there.

Fly-Fut is a Spanish company that records soccer games using drone technology. We edit the footage and add audio commentary, and we offer it through an app. We charge customers through a content-as-a-service model, through monthly and yearly subscriptions. I’m a chief product officer in the company.


What challenge were you trying to address with Machine Learning?

We implemented a machine learning model to pre-identify the events of a game so that video editors wouldn’t have to watch them in their entirety.


What was the scope of their involvement? developed the whole thing. We have an external machine learning consultant that helps us understand what is doing. They developed the entire model and took care of testing and deployment.

The machine learning model preselects 30–50 relevant events in a game, such as goals and saves, and puts them in a list, so that the editors only have to watch the events selected by the machine.

What is the team composition?

We started with three people on Eidos’s side, and I believe they’ve hired two more. Gonzalo (Co-Founder) is the project director on their side, Marcelo (Co-Founder) is the senior machine learning specialist, and they have a junior machine learning specialist.

How did you come to work with Machine Learning?

We found them via Upwork. We talked to multiple people, and we chose Eidos because they’re Spanish speakers, which was a big plus. Also, they’re based in Uruguay, and people there love football. They understood what the problems were going to be, and they made some proposals proactively. They proposed things that had relevance and made sense.

How much have you invested with them?

I had control over the budget during the MVP phase, but it’s gone over to our CTO now. I believe the total cost is in the neighborhood of $50,000.

What is the status of this engagement?

We started working with in February 2020, and the work is ongoing. We’re working with them on automating an entirely different process now. We’ve tackled the highlights part with great results, and we’re tackling the editing of full matches now. We want to make sure that people aren’t cutting the relevant parts of the matches.


What evidence can you share that demonstrates the impact of the engagement?

We’ve improved the efficiency of the video editing process fivefold. People are taking one fifth as much time to edit videos as they used to. We cut the relevant game pieces into 50-minute videos, and we also provide highlights of the main events.

How did Machine Learning perform from a project management standpoint?

We talk over Slack when something comes up, and we have weekly checkups. We close sprints every two weeks. Eidos is extremely helpful from a project management standpoint, and they document everything they do. We have our own wiki, and the code is commented perfectly. They track everything they do, and they’re proactive in proposing things.

Are there any areas they could improve?

They don’t have a big data specialist, and we’re facing a challenge with scale now because we’re recording around 2,500 games per month. We’ve just recently hired a cloud architect certified by Google, and he told us that what Eidos was proposing didn’t make sense from an architectural standpoint. Obviously, that had a very big impact on the budget we needed to allocate. Not having a big data specialist might be a problem from a scaling standpoint, but Eidos is very good on the math and machine learning side.

Do you have any advice for future clients of theirs?

Clients should set clear KPIs. People from Uruguay and Argentina are famous in Spain for talking a bit too much, and they can derail conversations. We’ve had to rail Eidos’s team to the key points of the conversation at times.

Overall Score
  • 4.5 Scheduling
  • 5.0 Cost
    Value / within estimates
  • 5.0 Quality
    Service & deliverables
  • 5.0 NPS
    Willing to refer