Orquesta

Orq.ai is a Generative AI Collaboration Platform that helps product teams build, deploy, and manage large language model (LLM) powered applications at scale. It provides an end-to-end solution to control GenAI, orchestrate prompts, evaluate models, and monitor performance across the entire AI development lifecycle.

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Overview

Orq.ai offers a unified platform to manage the lifecycle of LLM apps from prototype to production. It emphasizes collaboration across non-technical and technical roles, enabling teams to experiment, engineer prompts, deploy robust AI solutions, and observe costs, latency, and output in real time. The platform highlights ease of use, enterprise-grade security, and centralized control over multiple model providers and prompts.

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How It Works

• End-to-end tooling to scale LLM apps: from prompt engineering to deployment and observability.
• SDK & API access: seamless LLM orchestration with one line of code and support for Node and Python SDKs.
• Centralized governance: AI Gateway connects 150+ AI models and enables contextualized routing and privacy controls.
• Observability & tracing: track cost, latency, throughput, and output to optimize performance.
• Experimentation: compare models and prompts at scale before moving to production.
• RAG (Retrieval-Augmented Generation): contextualize LLMs with private knowledge bases.
• Prompt management: decouple prompts from code, manage iterative workflows, and reuse prompts across apps.

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Key Capabilities

• End-to-end platform to manage GenAI lifecycles
• Prompt engineering and management with decoupled prompts from code
• LLM orchestration via SDKs and a unified API gateway
• Evaluation and experiments to measure performance at scale
• LLM Observability for cost, latency, and output insights
• Tracing to debug complex pipelines
• RAG for private knowledge integration
• AI Gateway to connect multiple model providers or bring-your-own-models
• Case studies and best-practices to accelerate time-to-market

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How to Use Orq.ai

1. Assess and prototype: Use the Evaluator Library and Experiments to compare prompts and models.
2. Engineer prompts: Use Prompt Engineering tools to design and iterate prompts, independently from code.
3. Deploy: Move from prototype to production with end-to-end tooling and deployments.
4. Observe & optimize: Monitor cost, latency, and quality with LLM Observability and Traces.
5. Govern & scale: Route requests via AI Gateway and manage private data with privacy controls.

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Use Cases

• Accelerating time-to-market for AI-powered SaaS
• Collaborative prompt engineering across cross-functional teams
• Managing large families of prompts and multiple LLMs at scale
• Observability-driven optimization of GenAI workloads

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Product Modules

• End-to-end lifecycle tooling for LLM apps
• Prompt management: decouple prompts from code and manage iterative workflows
• LLM orchestration with Node and Python SDKs
• AI Gateway: access 150+ AI models or bring your own
• Experiments: compare LLMs and prompts at scale
• Evaluation: built-in evaluators to measure performance
• Traces: track events in LLM pipelines for debugging
• RAG: private knowledge bases for improved accuracy
• LLM Observability: cost, latency, and output monitoring
• Cost management and performance insights
• User-friendly collaborative UI for cross-functional teams

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Safety and Compliance

• Enterprise-grade security with data governance and privacy controls
• Centralized management to reduce risk and ensure consistent policy enforcement

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Core Features

• End-to-end platform to manage the AI development lifecycle in one place
• Collaborative UI enabling non-technical team members to participate in prompt engineering
• Time-to-market acceleration for GenAI-powered solutions
• Enterprise-ready security and data governance
• API and SDK access for developers (Node, Python) and one unified AI Gateway
• Evaluations, experiments, and observability to optimize models and prompts
• Tracing and debugging tools for complex LLM pipelines
• RAG integration with private data sources