Ceylon AI leverages large language models to translate your infrastructure needs into executable code and API calls. Simply describe the environment or service you want in plain English, and Ceylon AI crafts Terraform scripts or invokes cloud provider APIs directly. It supports AWS, Azure, GCP, Kubernetes, and more. Built-in drift detection alerts you when live infrastructure diverges from your declared state. Cost estimation, security scans, audit logs, and policy enforcement ensure your environments remain compliant, optimized, and reliable throughout their lifecycle.

CompliantLLM provides enterprises with an end-to-end compliance solution for large language model deployments. By integrating CompliantLLM’s SDK or API gateway, all LLM interactions are intercepted and evaluated against user-defined policies, including data privacy rules, industry-specific regulations, and corporate governance standards. Sensitive information is automatically redacted or masked, ensuring that protected data never leaves the organization. The platform generates immutable audit logs and visual dashboards, enabling compliance officers and security teams to monitor usage patterns, investigate potential violations, and produce detailed compliance reports. With customizable policy templates and role-based access control, CompliantLLM simplifies policy management, accelerates audit readiness, and reduces the risk of non-compliance in AI workflows.

Multi-Agent AI Orchestration allows teams of autonomous AI agents to work together on predefined or dynamic goals. Each agent can be configured with unique roles, capabilities, and memory stores, interacting through a central orchestrator. The framework integrates with LLM providers (e.g., OpenAI, Cohere), vector databases (e.g., Pinecone, Weaviate), and custom user-defined tools. It supports extending agent behaviors, real-time monitoring, and logging for audit trails and debugging. Ideal for complex workflows, such as multi-step question answering, automated content generation pipelines, or distributed decision-making systems, it accelerates development by abstracting inter-agent communication and providing a pluggable architecture for rapid experimentation and production deployment.

Solana MultiModal AI Agent is an open-source framework combining cutting-edge AI models—GPT for text, DALL·E for image, Whisper for audio transcription and synthesis, plus video generation—with the Solana blockchain. It provides a modular server architecture and RESTful API, enforcing per-request SOL payments on-chain. Developers configure their Solana wallet and OpenAI credentials, deploy the agent, then send multimodal requests via UI or API. Responses are delivered with associated transaction receipts. This design supports micropayments, auditability, and decentralized AI services, ideal for Web3 dApps and creative content platforms.

Integry AI Agent is a no-code integration and automation platform using advanced language models to let users design, deploy, and manage intelligent digital workers. Leveraging over 1000 pre-built connectors to apps like Salesforce, Slack, HubSpot, and QuickBooks, it transforms natural language instructions into end-to-end workflows without manual API coding. Users define triggers, actions, and conditional logic via a guided interface or chat-based prompts. AI-driven suggestions speed up flow creation, while real-time dashboards offer visibility into task execution, error handling, and performance analytics. Integry also enforces enterprise-grade security and compliance with role-based access control and audit logs, ensuring scalable automation for any team.

Secure Agent Augmentation provides a Python SDK and set of helper modules to wrap AI agent tool calls with security controls. It supports integration with popular LLM frameworks like LangChain and Semantic Kernel, and connects to secret vaults (e.g., HashiCorp Vault, AWS Secrets Manager). Encryption-at-rest and in-transit, role-based access control, and audit trails ensure that agents can augment their reasoning with internal knowledge bases and APIs without exposing sensitive data. Developers define secured tool endpoints, configure authentication policies, and initialize an augmented agent instance to run secure queries against private data sources.