Meta AI Integrations

Notenic is a runtime orchestration and governance platform designed to control and secure autonomous AI agents (“digital labor”) in real time, particularly in environments where failure carries regulatory, legal, or operational consequences. It operates as an infrastructure layer that sits directly in the execution path of AI systems, enforcing deterministic governance before any action reaches systems of record, rather than relying on post-output filters or prompt-level controls. It introduces a zero-trust runtime architecture built on core principles such as zero-persistence (no data retained after each session), execution-path control (policy enforcement at the moment of action), and independence from model context, ensuring that adversarial inputs cannot override governed behavior. Notenic provides a unified control plane that includes agent workforce management (treating AI agents as operational units with defined roles and supervision).

GuardionAI is an Agent and MCP Security Gateway that provides unified security for AI agents and Model Context Protocol tools operating on enterprise data. It sits in the execution path to discover, redact sensitive data, enforce protection, and give teams visibility into actions that traditional SIEM, DLP, and identity layers cannot see. Every agent action is inspected, enforced, and logged at the protocol level across AI agents, LLM apps, RAG systems, chatbots, coding agents, MCP servers, internal tools, databases, operating systems, and cloud environments. GuardionAI protects against critical AI threats such as prompt injection, system override, web attacks, MCP tool poisoning, malicious code execution, NSFW content, PII and credential exposure, confidential data leakage, off-topic drift, and unauthorized access, mapped to OWASP LLM Top 10 and agentic AI threat frameworks. Its gateway provides four layers of protection.

Pillar Security is a unified AI security platform for securing the agentic workforce across the entire AI lifecycle, from development to deployment and runtime protection. It connects business context across discovery, testing, and protection so security intelligence compounds across AI applications, agents, models, prompts, frameworks, tools, MCP servers, skills, coding agents, SaaS, cloud, code, and endpoints. Pillar helps organizations discover and manage AI assets everywhere, including shadow AI and unapproved systems, assess supply chain and posture risks, map agentic attack surfaces, and validate the vulnerabilities that actually matter. Its AI Security Posture Management capabilities analyze connected agents, tools, permissions, data sources, prompts, models, and supply chain components to expose risky paths, policy violations, misconfigurations, coding agent risks, and blast radius when a single component is compromised.

Muse Image is Meta’s image generation model from Meta Superintelligence Labs, built into Meta AI for creating, editing, and sharing high-quality visuals. The model can turn simple conversational prompts into detailed images, blend multiple photos together, remove unwanted objects, generate legible text inside visuals, and create styled outputs such as portraits, posters, stickers, room redesigns, infographics, and fantasy scenes. Muse Image uses advanced reasoning through Muse Spark to plan layouts, understand context, look up real-time web information, and combine visual references more intelligently. Users can start with suggested presets, mention Instagram accounts to personalize creations, and sketch or annotate edits directly on top of an image. The model powers creative experiences across Meta AI, Instagram Stories, WhatsApp chats, and soon Facebook, Messenger, and advertiser tools through Meta Advantage+ creative.

Muse Video is Meta’s upcoming video generation model from Meta Superintelligence Labs, previewed alongside the launch of Muse Image. The model is built on the same pretraining foundation as Muse Image and is designed to generate high-fidelity videos with native audio support. Muse Video focuses on prompt adherence, visual realism, temporal consistency, and the ability to create short scenes with clear motion, continuity, and audio context. It can generate a wide range of video styles, including cinematic footage, UGC-style ads, animal scenes, product commercials, handheld point-of-view clips, and realistic moments with sound effects, voices, and music. Meta is continuing to improve areas such as audio-video synchronization and physically accurate fast motion before broader release. Coming soon to creators and Meta AI, Muse Video is positioned as a powerful tool for generating dynamic media across Meta’s creative ecosystem.

Hive is the world’s first platform providing businesses with an end-to-end sustainability solution specifically built for the marketing industry. Hive’s proprietary mapping engine seamlessly integrates with any data source through APIs, automatically mapping data sets to globally recognized emission factors and industry standards, empowering organizations to compute precise carbon emissions.
 Hive's mapping engine measures all media delivery across your business and harmonizes the data sets needed to work with a brand and agency's methodology. Hive streamlines the process and also ensures accuracy in assessing and mitigating carbon footprints. Accessing Hive's suite provides you with comprehensive carbon emission tracking. Clients can effortlessly monitor emissions from all business operations, including media delivery by channel enabling informed decision-making. Stay ahead with Hive's intuitive platform.

Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices.

AEOBox is a platform that helps brands increase their visibility across AI-powered discovery systems like ChatGPT, Google AI Overview, Perplexity, Claude, Grok, Meta AI, and more. It combines strategic content creation, performance tracking, and AI visibility analytics to analyze how a business appears in generative AI search, generate AI-optimized content, and boost brand presence in the new era of AI search and discovery. AEObox enables companies to grow in the age of AI by improving how their brand, products, and expertise appear inside conversational AI tools and AI-powered search interfaces. The platform blends intelligent automation with human creativity to generate, distribute, and optimize content that AI systems can easily understand, surface, and trust. With built-in analytics and reporting, AEObox functions as a visibility engine that connects high-quality content to measurable inbound demand.