Gigaflop TechLab

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Established in 2014, Gigaflop TechLab is an AI development company. Their midsize team is in Indore, India. Their services include Artificial Intelligence, BI & big data consulting & SI, Generative AI, AI Agents, and more.

Client Insights

Industry Expertise

Medical

15%

eCommerce

15%

Education

10%

Financial services

10%

Hospitality & leisure

10%

Information technology

10%

Insurance

10%

Manufacturing

10%

Real estate

10%

Client Size Distribution

Small Business (<$10M) 50%

Midmarket ($10M - $1B) 30%

Enterprise (>$1B) 20%

Common Project Size

$10K-$49K 2 projects

Clients

  • Hotel AI Concierge

Highlights from Recent Projects

Overall Rating

5.0
2 Reviews

Gigaflop TechLab was hired by TutorScouts, an online tutoring and e-learning company, to develop a scalable AI-powered tutoring platform. Its aim was to improve the user experience, automate workflows, and optimize operational efficiency for future growth. Gigaflop TechLab was chosen for this project due to their high ratings and good value for cost, with a team of 2-5 employees being assigned to work on it.

The team at Gigaflop TechLab was responsible for the entirety of the project, undertaking tasks such as AI consulting, solution architecture, and custom software development. This also included third-party integrations, testing, deployment and post-launch support. The key deliverables were an intelligent tutor matching system, student and tutor dashboards, scheduling and booking management, secure payment integration, course management, analytics, and administration portal.

The results were highly positive. TutorScouts praised Gigaflop TechLab for their clear communication and adherence to deadlines. The high-quality, scalable, and secure platform they delivered enhanced the user experience and considerably supported TutorScouts' long-term growth objectives. The project is a testament to Gigaflop TechLab's commitment to delivering excellent and efficient solutions.

Timeliness

5.0

Service Excellence

5.0

Value

5.0

Would Recommend

5.0