Automating harmful content redaction
Netsafe, a nonprofit organization, uses Meta Llama models to develop a robust redaction tool for harmful digital communications. The tool employs a tuned LLM and the AI4Privacy dataset to automatically redact sensitive information, facilitating faster and more effective harm resolution. Netsafe's data pipeline, built on Dagster, Postgres, and BigQuery, sources, redacts, and aggregates data from various channels, reducing victim impact and augmenting the skills of Digital Harm Resolution Officers.
9
AI use cases in
Security
Flashpoint uses Gemini for Google Workspace to improve efficiency and productivity across its workforce, allowing employees to focus on the work that counts. They incorporate generative AI into workflows to enhance communication, ideation, research, and coding.
Palo Alto Networks is using Gemini to create a grounded AI assistant for 24/7 security platform support to improve agent efficiency and response time.
NetRise developed Trace to provide software supply chain security by introducing AI-powered intent-driven searches, allowing users to search their assets based on the underlying motives or purposes behind the code and configurations rather than solely relying on signature-based methods.
15
companies using
Security Agents
Palo Alto Networks is using Gemini to create a grounded AI assistant for 24/7 security platform support to improve agent efficiency and response time.
Fiserv, a developer of financial services technology, can now summarize threats, find answers, and detect, validate, and respond to security events faster with the Gemini in Security Operations platform.
NetRise developed Trace to provide software supply chain security by introducing AI-powered intent-driven searches, allowing users to search their assets based on the underlying motives or purposes behind the code and configurations rather than solely relying on signature-based methods.
49
solutions powered by
Meta
Roboflow uses Meta's Segment Anything Model (SAM) to enable users to automatically segment objects in images and videos, significantly reducing the time required to create training datasets for computer vision models.
Untukmu.AI, an online gifting site in Indonesia, uses Meta's Llama 3.1 8B model with split inference processing to protect customer privacy. By running part of the AI model on customers' devices and the rest on their servers, they deliver personalized gift recommendations without accessing or storing personal data. This ensures customer privacy while still providing high-quality, tailored suggestions, enhancing trust and satisfaction.
CodeGPT, a popular coding assistant with over 1.4 million downloads, integrates Meta's Llama models to enhance developer productivity. By using Llama 3.2 (90B), CodeGPT helps developers not just generate code but also answer questions about their codebase, debug code, and onboard new team members. It includes a codebase graph mechanism that lets Llama understand entire repositories, allowing developers to effectively "talk" with their code. This integration leads to at least a 30% increase in productivity and accelerates onboarding from months to days.
129
AI use cases in
Global
Intercom, a leading customer service platform, integrated Claude from Anthropic into their AI agent Fin to automatically resolve up to 86% of customer support queries with human-quality, personalized responses in over 45 languages. This implementation reduces response times from 30 minutes to seconds and enables human agents to focus on more complex issues.
StudyFetch built a comprehensive learning platform powered by Claude, providing personalized learning experiences through their AI tutor Spark.E. The platform analyzes lectures, generates study materials, and offers 24/7 tutoring support in over 20 languages, enabling students to master complex subjects with tailored, interactive assistance.
BBVA, a global financial institution, widely adopted ChatGPT Enterprise, creating over 2,900 custom GPTs within five months. By integrating ChatGPT into various departments, BBVA empowered its 125,000 employees to develop tailored AI solutions that enhance efficiency, spark creativity, and streamline workflows. This democratized AI access enabled rapid prototyping, reduced project timelines from weeks to hours, and facilitated the sharing of expert knowledge across the organization.