In episode 118 of the AI/Hyperautoation Minute, Toni Witt reviews startup Granica, the problems it has identified, solutions it has built, and its unique business model.
This episode is sponsored by “Selling to the New Executive Buying Committee,” an Acceleration Economy Course designed to help vendors, partners, and buyers understand the shifting sands of how mid-market and enterprise CXOs are making purchase decisions to modernize technology.
Highlights
00:32 — The startup Granica helps enterprises drive value through AI. Enterprise use cases for AI involve a massive amount of training data, which includes sensitive information.
01:11 — Training data is often not stored as efficiently as it could be. This results in skyrocketing cloud compute costs, more expensive training costs, longer development pipelines, and inherent risks, such as leaking sensitive information.
Which companies are the most important vendors in AI and hyperautomation? Check out the Acceleration Economy AI/Hyperautomation Top 10 Shortlist.
01:28 — Granica’s solution, Granica Crunch, is an API that reduces the size and the cost of petabyte-scale AI data in cloud object storage by up to 80% by using novel compression and de-duplication algorithms.
02:01 — “The startup is about lowering the barriers to entry for enterprise AI adoption by making training data itself and the training process more efficient and less costly,” Toni explains. The Granica team recently raised $45 million in VC funding.
02:33 — Toni notes that the startup’s “unique business model” is “a bit different for the rest of the industry.” Rather than charging per compute cost, like most other AI providers, Granica only charges a percentage of the actual savings of its customers. Toni calls its pricing structure “very value-based.”
02:53 — Granica enables enterprises to “not only improve the ROI of their AI initiatives and further incentivize larger training sets” but also “encourages lower-budget firms to also participate in this AI revolution.”
