Granica AI

What is Granica AI?

Granica AI is a modern Training Data Platform that aims to revolutionize AI team workflows by offering innovative products like Granica AI Crunch, Screen, and Chronicle AI. These tools are tailored to enhance model performance, prioritize cost control, and ensure data privacy. Granica AI enables companies to reduce storage and access costs significantly, de-identify sensitive datasets for safe utilization, and gain deeper insights into data usage. It adheres to strict compliance standards like SOC 2 Type 2 and operates within a Virtual Private Cloud to keep data secure within the user's environment. Noteworthy for its recognition as a 2023 Gartner Cool Vendor, Granica AI has demonstrated its capabilities by efficiently handling large volumes of data and generating substantial savings for prominent companies. The platform is designed to cater to both generative and traditional AI models, offering features such as advanced data reduction, enhanced data privacy tools, increased data visibility through natural language processing, cloud-scale operations, and outcome-based charges.

Who created Granica AI?

Granica was founded in 2019 with a $45M investment and is based in Mountain View, CA. The company was founded by a team including Deepak Ahuja, former CFO of Tesla, Kevin Hartz, Co-Founder of Eventbrite, and Frederic Kerrest, Executive Vice Chairman of Okta, amongst other notable figures in business and tech. Granica focuses on developing cutting-edge AI efficiency solutions to improve data management and AI model performance.

What is Granica AI used for?

• Compress data lakes to reduce storage and access costs
• De-identify sensitive data for safer model training
• Utilize natural language processing to explore data and gain insights quickly
• Handle hundreds of petabytes of data within VPC environments
• Costs based on efficiency and outcomes rather than data consumption
• Improve AI model performance
• Enhance AI model training and fine-tuning processes
• Guarantee SOC 2 Type 2 compliance
• Operate within a Virtual Private Cloud to ensure data never leaves the user's environment
• Significant cost savings reported by clients
• Explore data and gain insights quickly with natural language processing
• Handle petabyte-scale demands efficiently
• Reduce costs to store and use expanding data sets via data compression
• Unlock information in cloud data lakes for model training via data privacy and masking
• Improve model performance and business outcomes directly
• Make AI safer to use, more efficient, and effective by increasing the signal-to-noise ratio of information
• Blend fundamental research in information science and machine intelligence with distributed systems engineering
• Explore and exploit statistical correlations in streams of bytes with high accuracy at cloud-scale
• A platform that scales up to handle hundreds of petabytes maintaining data within VPC environments
• Outcome-Based Charges: Costs based on efficiency and outcomes rather than data consumption
• Improve AI model performance by enabling efficient data operation
• Reduce costs and ensure data privacy for generative and traditional AI teams
• Enhance AI model training and fine-tuning processes through data reduction, data privacy, and data visibility
• Adhere to security and privacy standards like SOC 2 Type 2 compliance and operating within a Virtual Private Cloud
• Help clients achieve major savings like a 50% reduction in S3 data costs and a 10x reduction in S3 API costs
• Compress data lakes to reduce storage and access costs up to 80%
• Utilize AI-optimized tools to de-identify sensitive data for safer model training
• Scale up to handle hundreds of petabytes maintaining data within VPC environments
• Improving AI model performance by enabling efficient data operation, reducing costs, and ensuring data privacy for generative and traditional AI teams
• Enhancing AI model training and fine-tuning processes through data reduction, data privacy, and data visibility features
• Guaranteeing SOC 2 Type 2 compliance, operating within a Virtual Private Cloud, and ensuring data never leaves the user's environment for security and privacy
• Reported savings include a 50% reduction in S3 data costs for a client and a 10x reduction in S3 API costs for another client
• Initiating usage by requesting a demo or contacting the Granica team for more information and starting the process

Who is Granica AI for?

• AI researchers
• Data scientists
• Systems engineers
• ML engineers
• Researcher
• Systems engineer
• ML Engineer
• Machine learning engineers

How to use Granica AI?

To use Granica, follow these steps:

1. Understand the Platform: Granica is a Training Data Platform designed to improve AI model performance through efficient data operations, cost reduction, and data privacy for both generative and traditional AI teams.

2. Features:

   • Data Reduction: Compress data lakes to reduce storage and access costs by up to 80%.
   • Data Privacy: Utilize AI-optimized tools to de-identify sensitive data for safer model training.
   • Data Visibility: Explore data and gain insights quickly using natural language processing.
   • Cloud-scale Operations: A platform that scales up to handle large amounts of data within Virtual Private Cloud environments.
   • Outcome-Based Charges: Costs are based on efficiency and outcomes rather than data consumption.

3. Getting Started: Request a demo or contact the Granica team through their website to begin exploring the platform and its capabilities.

4. Security and Compliance:

   • Granica adheres to SOC 2 Type 2 compliance.
   • Data is operated within a Virtual Private Cloud to ensure data remains in your environment.

5. Pricing and Clients:

   • Clients have reported significant cost savings, such as a 50% reduction in S3 data costs for Here Technologies and a 10x reduction in S3 API costs for Nylas.

6. Future Products: Granica aims to continue helping data teams improve model performance and business outcomes while ensuring data privacy and cost efficiency. They are dedicated to increasing the signal-to-noise ratio of information.

7. Investment and Backing: Granica is supported by notable investors in AI, Data, and Cloud, emphasizing their commitment to building the future of AI efficiency.

By following these steps and exploring the features and benefits of Granica, you can effectively leverage the platform to enhance your AI model performance while prioritizing data privacy and cost control.