Orq.ai

What Orq.ai does

Orq.ai is a Generative AI Collaboration Platform that enables software teams to operate agentic AI systems at scale. It addresses the tooling gaps in the GenAI development lifecycle, providing specialized technical capabilities and collaborative functionalities. The platform allows developers and non-developers to streamline workflows and scale agentic AI systems from prototype to production. It focuses on Agent Lifecycle Management, including building, observing, evaluating, and continuously improving AI agents. Key offerings include an AI Gateway for routing requests across models, AI Observability for monitoring agent performance, Optimization for running evaluations, AI Governance via Control Tower, and a Shared Library for reusable AI components. Orq.ai is designed to bridge the gap between engineers and non-technical teams in GenAI product development.

Products

• Agent Lifecycle ManagementA comprehensive system for building, observing, evaluating, and continuously improving AI agents, ensuring quality gates and performance metrics.
• AI GatewayRoutes requests across 400+ models from 28+ providers with built-in fallbacks, retries, and cost visibility. It acts as a control hub for managing and optimizing API calls to LLMs.
• AI ObservabilityProvides full traces across multi-step agents and tool calls, tracking latency, token spend, and failure modes in real time.
• Optimization (Evals)Enables running offline and online evaluations on every change, comparing versions side by side on key metrics to ensure quality and confidence in shipping.
• AI Governance (Control Tower)Offers visibility into every agent, tool, and model running across an organization, monitoring cost, compliance, and risk in one place.
• Shared LibraryA single source of truth for prompts, skills, MCPs, tools, and knowledge, organized in repositories, reusable across teams, versioned, and ready to deploy.
• Orq MCP & SkillsAllows querying traces and running experiments from any coding agent in plain English, providing direct access to the Orq.ai workspace with 38 specialized tools.
• IdentitiesTies every trace to a user or tenant, enabling per-identity quotas and cost attribution.
• Feedback APICaptures ratings, corrections, and structured reviews on any trace with minimal code.
• Auto RouterRoutes every request to the best model based on cost, latency, or quality.
• Agent RuntimeDeploys and manages autonomous agents with built-in tools, memory, and orchestration, handling the execution layer and providing production-grade scalability.
• Knowledge Base (RAG-as-a-Service)Manages, stores, searches, and serves knowledge for Retrieval-Augmented Generation, supporting multi-format content, smart chunking, model flexibility, and RAG evaluation.
• LLM and Prompt EvaluationA suite of evaluation tools including RAG evals, LLM-as-a-judge, Python evals, agent evals, regression testing, prompt experiments, model experiments, and human evaluation.
• Observability & MonitoringFeatures tracing of AI interactions, advanced tracing with custom events and metadata, smart automation rules, dashboards & analytics, feedback loops, and data & privacy management with PII filtering.

Key capabilities

• Agent Lifecycle Management
• One API. Every model.
• AI Observability
• Optimization (Evals)
• AI Governance (Control Tower)
• Shared Library
• Framework agnostic (LangGraph, OpenAI Agents, CrewAI, Vercel AI, OpenTelemetry)
• Orq MCP & Skills
• Identities (user/tenant tracing, quotas, cost attribution)
• Feedback API
• Auto Router (cost, latency, quality based routing)
• Data Residency (EU or chosen region, sensitive field masking)
• Enterprise security (SSO, RBAC, audit logs, compliance paperwork)
• Deployment methods (cloud, hybrid, on-premise, private connections)
• Agent Orchestration
• Production-Grade Scalability
• Tool & API integration
• Memory & Context (persistent memory, structured knowledge bases)
• Safety, Quality & Observability (tracing, guardrails, tool approvals, online evaluation)
• Human-in-the-Loop (approve, correct, guide agent actions)
• Developer-First APIs (SDKs, clean APIs)
• Multi-Format support (PDFs, TXT, DOCX, CSV, XLS for knowledge bases)
• Smart chunking (adaptive, Chunking API, semantic, recursive, hierarchy-based)
• Model Flexibility (embedding models, rerankers)
• Agentic RAG
• Retrieval tuning (reranking, multi-hop reasoning)
• RAG Evaluation (groundedness checks, relevance scoring)
• Retrieval Observability (quality, latency, drift, usage patterns)
• Retrieval Transparency (source attribution, chunk-level traceability)
• Live Data Access (external knowledge bases, dynamic KB)
• Evaluation Framework (mix RAG evals, LLM-as-a-judge, Python-based logic)
• Experimentation (test every change, catch regressions)
• Agent Performance Evaluation (reasoning, decision quality, multi-step behavior)
• Human evaluation (route responses to reviewers, collect annotations)
• Datasets (versioning, lineage, golden datasets)
• Guardrails (block unsafe outputs, enforce policies, PII detection)
• Evaluation Dashboards (quality, drift, latency, costs monitoring)
• Evaluator Library (prebuilt and extensible evaluators)
• Tracing (LLM call, tool action, agent step, execution paths, token-level insight)
• Advanced TRACING (custom events, metadata, user IDs, request context)
• Smart Automation Rules (trigger workflows on patterns)
• Dashboards & Analytics (latency, errors, token usage, routing efficiency, drift)
• Feedback Loops (user ratings, issues, evaluation loops)
• Data & Privacy Management (privacy rules, retention policies, anonymization)
• Privacy-First Logging (PII and sensitive data masking)
• SDK-Ready Observability (OpenTelemetry, LangChain, Autogen, OpenAI Agents, Google AI SDKs integration)

Use cases

• Scaling agentic AI systems from prototype to production
• Building and deploying intelligent agents with memory, tools, and real-time execution
• Monitoring and optimizing AI agent performance
• Ensuring compliance and managing risk in AI deployments
• Collaborative GenAI product development workflows
• Replacing internal AI Gateway setups for governance, scalability, and cost-monitoring
• Accelerating AI development workflows
• Automating customer tickets and support
• Building custom co-pilots for clients
• Improving collaboration between technical and non-technical teams in AI development
• Managing prompt templates and configurations for complex production cases
• Developing healthcare AI assistants
• Scaling customer support without increasing headcount
• Building AI solutions for clients in consulting firms
• Creating AI-powered voicebots
• Optimizing LLM output with custom RAG workflows
• Measuring and optimizing AI products
• Running and coordinating autonomous agents
• End-to-end insights into the performance and traces of agents

AI approach

Orq.ai provides a Generative AI Collaboration Platform for software teams to operate agentic AI systems at scale. It offers tools for agent lifecycle management, AI observability, optimization, and governance. The platform routes requests across 400+ models from 28+ providers, supports various AI frameworks, and enables continuous evaluation and improvement of AI agents. It focuses on bridging the gap between engineers and non-technical teams in GenAI product development.

Tech named: LLMs, LLMOps, Generative AI, GenAI, Artificial Intelligence, AI, LangGraph, OpenAI Agents, CrewAI, Vercel AI, OpenTelemetry, Orq MCP & Skills, Feedback API, Auto Router, vector database, RAG, LangChain, LlamaIndex, Autogen, Google AI SDKs, Pydantic AI

Industries served

• Software Development
• Digital Health
• Energy Technology
• Consulting

What it says sets it apart

• Designed for Generative AI Collaboration, bridging engineers and non-technical teams
• Comprehensive Agent Lifecycle Management (Build, Observe, Evaluate, Improve)
• One API for 400+ models from 28+ providers with built-in fallbacks and retries
• Enterprise-grade AI Governance (Control Tower) for cost, compliance, and risk monitoring
• Framework agnostic, integrating with popular AI frameworks (LangGraph, OpenAI Agents, CrewAI, Vercel AI, OpenTelemetry)
• Strong focus on data residency, enterprise security, and EU compliance (SOC 2, GDPR, EU AI Act ready)
• Flexible deployment options (cloud, hybrid, on-premise, private connections)
• Production-grade scalability for thousands of concurrent agents
• Human-in-the-Loop capabilities for safety and accuracy
• RAG-as-a-Service with multi-format support, smart chunking, and retrieval transparency
• Extensive LLM and prompt evaluation framework with human and automated evals
• Privacy-first logging with PII and sensitive data masking
• Built in Europe, headquartered in Amsterdam, with a global team.

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.