Granica, the information and computer science company working on the hardest problems in data today, is emerging from stealth—backed by $45M in funding—and introducing its cloud native AI efficiency platform, built to put valuable, data-centric AI research into the hands of enterprises. By revolutionizing AI efficiency through data reduction and data privacy, the Granica API optimizes AI training data capture, storage, and usability, driving enterprise AI profitability and innovation, according to the company.
The launch of Granica responds to the ever-increasing volumes of training data that many organizations are forced to reckon with, compounded further by concerns of AI ethicality and security. Though many think more data is good data, AI is only as effective as the data it's trained on, placing great importance on its usability, scalability, and efficiency—the key areas that Granica is tackling.
“Our mission is to enable enterprise AI teams to maximize the value of their data and keep much more, if not all, of their AI data ‘hot.’ This is the key to unlocking the transformative potential of artificial intelligence and machine learning,” said Rahul Ponnala, co-founder and CEO of Granica. “Data fuels the AI engines that are quickly becoming essential to modern commerce, science, and everyday life. Just look at the sudden explosion in generative AI tools to get a sense of the future reach of this technology.”
Granica physically reduces the size and cost of petabyte-scale AI training data in cloud object stores by up to 80% via innovative compression and deduplication algorithms, according to the company. In terms of security, Granica preserves the privacy of sensitive information in object data, overall improving data security posture while propelling safe AI usage. The solution maximizes AI training data, secures its sensitive components, and mitigates redundancy and other cost inefficiencies.
“There is a huge efficiency gap for AI workloads, especially for training data in cloud object stores like Amazon S3 and Google Cloud Storage. Making data more efficient requires an entirely new layer in the AI stack consumed as an API and directly integrated with AI applications,” said Andrea Montanari, chief scientist at Granica. “At its core, what we’re doing at Granica is fusing fundamental data-centric AI research with large-scale systems engineering expertise to build a platform that drives AI information density and efficiency at cloud scale.”
The API integrates directly with cloud applications to optimize downstream pipelines and model activity, rectifying the damage caused by the rapid growth of training data with low information value. Granica offers the following AI efficiency services:
- Granica Crunch, a data reduction service for enterprise AI that supplies byte-granular data reduction, capable of reducing storage and write costs
- Granica Screen, the data privacy service for enterprise AI that supplies byte-precise detection for high recall and high precision identification and protection of sensitive data
Granica’s platform is free to deploy with no upfront costs. Upon measuring how much Granica Crunch reduces storage costs relative to the Amazon S3 and GCS baseline, the cloud costs produced by the user’s environments are covered by the baseline savings. A small portion of the resulting savings outcome is covered by the Granica customer, where customers pay only for the value received.
“Instead of charging users based on consumption, we measure and charge for the outcomes Granica’s efficiency services deliver within the customer’s environment. The amount saved from using Granica puts dollars right back into organizations' AI-based innovations—or directly to their bottom line,” explained Ponnala. “Simply put, Granica’s incentives to drive AI efficiency are aligned with our customers. The more value we can deliver from every byte of data, the more our customers improve their ROI on AI, and the more we earn. It’s a total win-win.”
To learn more about Granica, please visit https://www.granica.ai/.