2018, …, 2021, 2022! A review of our path

December and January are the months where we meditate about the path we just covered and the one we are discovering. Below, you will find a coarse picture of our path. Although it hasn’t been a flat straight path, its direction hasn’t changed. We keep committed to building a knowledge-driven reliable ecosystem to make AI projects flourish. Our long-term vision remains the same, to become an incubator for AI projects. This means building robust and reproducible ways of tackling data-driven projects. We have standardized how we approach the most common open questions that any data project faces, from ideation to productization. We have created a proprietary approach to tackle AI projects, and it has been reflected on a continuously increasing customer base, successfully validating AI projects in the shortest time possible.

Sustained Growth

Last year, we had 47% more customers than in 2020, with revenue increasing on an annualized revenue growth of around 45%. All of this, despite the pandemic situation of the last two years that shifted company budgets from AI innovation to merely adapting to the new reality while guaranteeing minimum operational levels. This growth has been entirely organic, relying on revenue obtained from internal projects and initiatives without any dependency on external funding.

We entered into new industries (now including transportation, RegTech, and retail) and increased our presence in industries already approached (automotive, real estate, healthcare, telecommunications, financial services, among others).

Key Achievements

One of the key remarks from last year was winning the Flat Finder challenge promoted by DataHub Ruhr (link, link), which allowed us to strengthen our position in the German market. We are thankful for the trust deposited into us by Vonovia, the corporate partner promoting the challenge.

The second highlight of last year was the incorporation of NILG.AI LLC. This will allow us to serve better our US customer base, a key market where we have a constantly increasing presence.

Knowledge is our core value

NILG.AI’s value proposition is centered around knowledge, not only the knowledge that our ML models can obtain from the data 😉 but the knowledge that our team systematically builds around AI. Last year, we promoted 60 joint learning sessions (+1/week) where all our data scientists gathered to discuss a new topic. We discussed many theoretical concepts (e.g., uplift modeling, GANs, Federated Learning), tools (DVC, Nomad, Great Expectations), and business problems through our AI use cases. When you decide to partner with us, you can rest assured that you gained access to an entire team obsessed with bringing the latest advances to your project. Each of our team members devotes at least an afternoon per week to becoming a better professional through a moment of learning.

But knowledge is not something you keep to yourself; it’s something you share and expand as a community. Therefore, we participated in 12 community events, published new scientific research, and sponsored magnificent events such as the VISUM summer school, a summer school in Computer Vision and Machine Learning that you shouldn’t miss.

A Final Note

Last but not least, I would like to personally thank all NILG.AI team members (current and former). Thank you for the passion you put into all your activities. NILG.AI has undoubtedly grown thanks to you. I personally hope you have professionally grown together with us.


Stay tuned for more!


Like this story?

Subscribe to Our Newsletter

Special offers, latest news and quality content in your inbox once per month.

Signup single post

This field is for validation purposes and should be left unchanged.

Recommended Articles

How to deal with the annoying implications of changing data sources

Let’s discuss a common scenario in AI consulting. The client provides access to data sources in formats such as CSVs or databases that aren’t in a production environment. Why? Usually, they’re exploring the value of the project, do not want to disclose too much data and want to prevent technical problems from happening at the […]

Read More
Stop removing outliers just because!

Outliers are data points that stand out for being different from the remaining data distribution. An outlier can be: An odd value in a feature A data point distant from the centroid of the data A data point in a region of low density, but between areas of high density. Suppose you have been working […]

Read More
Insights from UPTEC NON STOP

Last month, we participated in UPTEC NON-STOP, an event celebrating the 15th anniversary of UPTEC. UPTEC is the Science and Technology Park at the University of Porto, where businesses and academia meet to exchange ideas. During the event, local startups incubated at UPTEC (NILG.AI included), and French startups promoted by Accelerateur M pitched their business to […]

Read More