Numlabs

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Artificial intelligence company Numlabs was established in 2019. The small team focuses on Artificial Intelligence, BI & big data consulting & SI, IoT development, and logistics & supply chain consulting. The company is located in Kraków, Poland.

Client Insights

Industry Expertise

Business services

20%

Financial services

20%

Media

20%

Medical

20%

eCommerce

20%

Client Size Distribution

Small Business (<$10M) 35%

Midmarket ($10M - $1B) 50%

Enterprise (>$1B) 15%

Common Project Size

$10K-$49K 3 projects

$50K-$199K 2 projects

<$10K 1 project

Clients

This provider has not added their key clients.

Highlights from Recent Projects

Overall Rating

4.9
6 Reviews

Numlabs collaborated with Apriside, a software company catering to the oil and geothermal industries, in enhancing AI and machine learning functionalities. The team of 3-5 people from Numlabs, which included testers, project managers, and developers, worked closely with Apriside's developers on general coding, frontend development, and logic. They utilized various Python libraries to create machine learning algorithms based on Apriside's data and requirements. The partnership, which began in November 2019, has cost Apriside between $60,000 and $70,000 and is still ongoing.

Numlabs was instrumental in the development of the entire IT infrastructure for Testasy, an at-home diagnostics platform. They transformed an improvised solution into a custom solution covering every aspect of the platform, which allows multiple users, including patients, doctors, supervisors, and administrators, to interact. The platform, which has an entire database of patients, is connected to Slack and has API integrations with multiple governmental and certificate production systems. The engagement, which started in May 2021, has cost Testasy around $30,000 and is still ongoing.

Numlabs provided comprehensive training to the Geodesy and Offshore Survey Department at the Maritime University of Szczecin. The training, aimed at enhancing the team's knowledge and application of machine learning and deep learning methods, covered Python programming, agile methodologies, version control systems, and aspects of deep learning. The training was divided into three phases, including a week-long stationary training, online programming problem sets with individual feedback, and another week-long stationary training. The department plans to conduct a full-scale data science project as a concluding review of all the topics covered. The best offer was selected through a tender inquiry procedure.

Timeliness

4.6

Service Excellence

4.9

Value

4.9

Would Recommend

4.9