NILG.AI

Introduce your business and what you do there.

I’m the CTO of Pentadata. We’re a startup platform for financial data processing.

What challenge were you trying to address with NILG.AI?

We were in need of developing algorithms based on machine learning. We hired them as developers.

What was the scope of their involvement?

NILG.AI used Python and some Bash to develop the algorithms. They also handled the deployment of the algorithms.

What is the team composition?

I work with Kelwin (CEO) and Paulo (Data Scientist).

How did you come to work with NILG.AI?

We found them through a well-known marketplace website. I first interfaced with Kelwin and quickly discovered that he was very competent. That was an immediate guarantee of quality to me.

How much have you invested with them?

We’ve invested a few thousand dollars.

What is the status of this engagement?

Our ongoing engagement started in September 2020. We released the product in January 2021, but the collaboration has not ended.

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

NILG.AI is really good. They stand apart from other vendors I’ve worked with in the past. Their team was better in terms of the feedback they provide to their employees and the quality of the product.

How did NILG.AI perform from a project management standpoint?

Project management is very smooth. We work in an agile environment. It was nice that once Paulo was assigned to our project, that never changed. It made the process very easy because he knew everything. We usually communicate through video calls or emails.

What did you find most impressive about them?

Kelwin is so knowledgeable about machine learning. It’s a very broad field, and he’s an expert. Because of this, they come up with ideas and solutions that I wouldn’t have thought of.

Are there any areas they could improve?

No, I can’t think of anything.

Do you have any advice for potential customers?

I encourage you to share your business needs, not just your technical ones, with Kelwin and his team at the very beginning of your engagement.

Please describe your company and your position there.

MobileODT, makes small, smart, Internet-connected medical devices that health care providers can use at the point-of-care for a variety of examinations.

Our leading product is the Enhanced Visual Assessment (EVA) System for colposcopy, used for women's health services around the world.

EVA enhances the native capabilities of a mobile phone, and we use the phone's native communication and processing capabilities to create a telemedicine and analysis platform that supplements a health provider's skills to enable any provider, anywhere, to conduct an examination that until now can only be conducted by an expert physician. 

I was the co-founder and CEO during the course of the project.

For what projects/services did your company hire NILG.AI?

Nilg.ai did extraordinary work exploring the limits of ML approaches to cervical cancer detection, and came out with a number of practical insights that deeply affected the field of cervical cancer image processing as a whole.

What were your goals for this project?

To develop AI for cervical cancer imaging.

How did you select NILG.AI?

Kelwin Fernandez is one the world's experts at applying ML to develop AI for Women's Health Diagnostics. He assembled an extraordinary team and we were very lucky to have found them!

Describe the project in detail.

NILG.AI worked closely as an extension of our R&D team to develop new approaches on a spring-by-sprint basis. This included open-ended exploration as well as practical pilots and feature development.

What was the team composition?

We benefitted from a growing network of excellent ML professionals, led by Kelwin, who is a world leader.

Can you share any outcomes from the project that demonstrate progress or success?

While the final product is still in development, a number of the models developed my NILG.AI were deployed in the field in pilots and clinical trials, and were reviewed with great fanfare by the US National Cancer Institute.

How effective was the workflow between your team and theirs?

We worked fluidly and effectively with Kelwin's team. Excellent folks.

What did you find most impressive about this company?

There was no problem to small or too large - and they took on the challenges with enthusiasm and an understanding of why they were important to us.

Are there any areas for improvement?

At times there were challenges on our side in developing clear outcomes and defining the challenges we held at a higher priority than others.

Communication from our side could have been improved - and perhaps due to the desire of NILG.AI to be useful in 'filling the gaps' we did not learn of the lack of clarity until the process was sometimes midway.