Mexasol

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Mexasol is an AI firm headquartered in Lahore, Pakistan. They specialize in Artificial Intelligence, ai consulting, generative ai, data annotation services, and more and were established in 2021.

Client Insights

Industry Expertise

Information technology

90%

Telecommunications

10%

Client Size Distribution

Small Business (<$10M) 25%

Midmarket ($10M - $1B) 75%

Common Project Size

$10K-$49K 3 projects

<$10K 1 project

Clients

This provider has not added their key clients.

Highlights from Recent Projects

Overall Rating

3.9
4 Reviews

Mexasol was hired by Eminent Physicians, a healthcare technology company, to develop an intelligent system for a nutrition coach. The team of 6-10 employees from Mexasol was tasked with creating machine-learning nutrition solutions that can formulate diet plans, ensuring data authenticity from USDA, migrating patient data to the cloud, and developing an application that functions like a nutrition coach. They also built a dashboard and admin panel for weightloser.com and an intelligent food image recognition system. The company chose Mexasol for their good value for cost and aligned company values.

Slashdot Media, an internet publishing company, engaged Mexasol to build an AI development project. The project involved 2-5 Mexasol employees and was part of the Google News Initiative. The company found Mexasol through an online search and chose them for their budget-friendly pricing, good value for cost, and aligned company values. The deliverables were not specified in the data provided.

Truck Centers, Inc., a heavy-duty truck parts supplier, hired Mexasol for AI consulting. The project, carried out by 2-5 Mexasol employees, involved conducting a discovery workshop session and proposing a viable AI solution to implement. The company found Mexasol through an online search and selected them for their budget-friendly pricing, great culture fit, and good value for cost. The deliverables were not specified in the data provided.

Timeliness

3.9

Service Excellence

3.9

Value

3.8

Would Recommend

3.9