Orq.ai is a Generative AI Collaboration Platform that helps AI teams develop, ship, and optimize AI applications at scale. By delivering the tooling needed to operate large language models (LLMs) out of the box in a user-friendly interface, Orq.ai enables teams to build reliable AI apps from the ground up, run them at scale, control output in real time, and optimize performance.
Launched in February 2024, Orq.ai is on a mission to bridge the gap between engineers and non-technical teams during AI product development workflows so that everyone can actively participate in the transformative power of Generative AI regardless of their coding knowledge.
Here's an overview of our platform's core capabilities:
1. Generative AI Gateway: Integrate seamlessly with 130+ AI models from top LLM providers. That way, organizations can use or test different model capabilities for their AI use cases within one platform.
2. Playgrounds & Experiments: Test and compare AI models, prompt configurations, RAG-as-a-Service pipelines, and more in a controlled environment. This helps AI teams experiment with hypotheses regarding their AI application and assess quality before moving into production.
3. AI Deployments: Move AI applications from staging to production environments against built-in guardrails, fallback models, regression testing, and more for dependable AI deployments.
4. Observability & Evaluation: Monitor the performance of your AI in real-time through detailed logs and intuitive dashboards. Integrate programmatic, human, and custom evaluations to measure your AI and optimize performance over time.
5. Security & Privacy: Orq.ai is SOC2-certified and compliant with GDPR and the EU AI Act to support companies with data security and privacy regulations.
Seller
Orq.aiLanguages Supported
English, Dutch, Spanish
Product Description
Orquesta enables companies to integrate and operate their products using the power of Large Language Models through a single collaboration platform.
The platform centralizes prompt management, streamlined experimentation, feedback collection, and real-time insight into performance and costs. It's compatible with all major Large Language Model providers, ensuring transparency and scalability in LLM Ops, ultimately leading to shorter customer release cycles and reduced costs for both experiments and production environments.
For more information, please visit https://orquesta.cloud.
Overview by
Sohrab Hosseini
