Orq

Orq.ai is an enterprise-grade platform designed to accelerate the development and deployment of generative AI applications while maintaining stringent control, security, and governance. It provides a unified, collaborative workspace where development, operations, and business teams can build, test, ship, and monitor AI solutions with confidence. The core value proposition lies in its ability to streamline the entire AI lifecycle—from initial prompt engineering to production-scale deployment and ongoing model management—within a single, secure environment that prioritizes data privacy, safety, and operational oversight.

Key features: The platform offers a comprehensive suite of capabilities for managing the GenAI stack. This includes collaborative prompt engineering and versioning tools, LLM safety guardrails and automated AI safety checks to prevent harmful outputs, and robust data management with a focus on privacy. For deployment and operations, it provides model management with version control, retrieval-augmented generation (RAG) pipeline support, and system observability for monitoring application performance and costs. It also supports on-premise or private cloud hosting for enterprises with strict data residency requirements, alongside detailed model governance, audit trails, and retraining support workflows.

What sets Orq.ai apart is its deep focus on enterprise security and governance, which is often an afterthought in other AI development platforms. It is built from the ground up to handle sensitive data and comply with regulatory standards, offering features like granular access controls, comprehensive audit logs, and the ability to deploy in isolated environments. Technically, it integrates with a wide range of LLMs and vector databases, providing a model-agnostic framework that allows teams to switch or combine models without vendor lock-in. Its emphasis on LLMOps (Large Language Model Operations) provides the necessary tooling for continuous integration, delivery, and monitoring of AI applications, making the transition from prototype to production significantly smoother and more reliable.

Ideal for enterprise development teams, AI product managers, and ML engineers who need to build and scale secure, production-ready generative AI applications. Specific use cases include developing internal AI assistants with access to proprietary company data, creating customer-facing chatbots with built-in safety filters, and building complex RAG applications for knowledge management in regulated industries like finance, healthcare, and legal services. It is particularly valuable for organizations that require full control over their AI stack, data sovereignty, and demonstrable compliance with internal and external governance policies.

Pricing follows a freemium model with a free tier for individuals and small teams to get started, while enterprise plans with advanced security, support, and hosting options are available through custom quotes based on usage scale and required features.