Knowledge Base
Reliable RAG pipeline without the overhead
Power AI apps and agents with context from your private data. No custom pipelines needed. Orq.ai handles your entire RAG infrastructure so you don’t have to.
Knowledge Base
Reliable RAG pipeline without the overhead
Power AI apps and agents with context from your private data. No custom pipelines needed. Orq.ai handles your entire RAG infrastructure so you don’t have to.
Knowledge Base
Reliable RAG pipeline without the overhead
Power AI apps and agents with context from your private data. No custom pipelines needed. Orq.ai handles your entire RAG infrastructure so you don’t have to.
ingest
configure
retrieve
Enterprise-Ready RAG
Everything you need to store, search, and serve knowledge
Enterprise-Ready RAG
Everything you need to store, search, and serve knowledge
Enterprise-Ready RAG
Everything you need to store, search, and serve knowledge
Multi-Format support
Bring all your knowledge into one place
Upload PDFs, HTML, Markdown, text files, product docs, and structured data. Orq.ai ingests and indexes everything automatically, turning your content into a ready-to-query knowledge base.
Multi-Format support
Bring all your knowledge into one place
Upload PDFs, HTML, Markdown, text files, product docs, and structured data. Orq.ai ingests and indexes everything automatically, turning your content into a ready-to-query knowledge base.
Multi-Format support
Bring all your knowledge into one place
Upload PDFs, HTML, Markdown, text files, product docs, and structured data. Orq.ai ingests and indexes everything automatically, turning your content into a ready-to-query knowledge base.
Multi-Format support
Bring all your knowledge into one place
Upload PDFs, HTML, Markdown, text files, product docs, and structured data. Orq.ai ingests and indexes everything automatically, turning your content into a ready-to-query knowledge base.
Multi-format
content extraction
knowledge api
Smart chunking
Chunking API
data cleaning
Data Ingestion
Chunk smarter for better retrieval
Use Orq.ai’s adaptive chunking or call the Chunking API for full control. Mix semantic, recursive, or hierarchy-based strategies to get cleaner chunks and more relevant retrieval results.
Data Ingestion
Chunk smarter for better retrieval
Use Orq.ai’s adaptive chunking or call the Chunking API for full control. Mix semantic, recursive, or hierarchy-based strategies to get cleaner chunks and more relevant retrieval results.
Data Ingestion
Chunk smarter for better retrieval
Use Orq.ai’s adaptive chunking or call the Chunking API for full control. Mix semantic, recursive, or hierarchy-based strategies to get cleaner chunks and more relevant retrieval results.
Data Ingestion
Chunk smarter for better retrieval
Use Orq.ai’s adaptive chunking or call the Chunking API for full control. Mix semantic, recursive, or hierarchy-based strategies to get cleaner chunks and more relevant retrieval results.
Model Flexibility
Pick the models that match your data
Choose embedding models and rerankers from Orq.ai’s model garden or plug in your own. Tailor retrieval behavior to your domain without modifying your application logic.
Model Flexibility
Pick the models that match your data
Choose embedding models and rerankers from Orq.ai’s model garden or plug in your own. Tailor retrieval behavior to your domain without modifying your application logic.
Model Flexibility
Pick the models that match your data
Choose embedding models and rerankers from Orq.ai’s model garden or plug in your own. Tailor retrieval behavior to your domain without modifying your application logic.
Model Flexibility
Pick the models that match your data
Choose embedding models and rerankers from Orq.ai’s model garden or plug in your own. Tailor retrieval behavior to your domain without modifying your application logic.
multi-model
Embedding models
Reranking models
Agentic RAG
Retrieval tuning
Reranking
Retrieval tuning
Tune your RAG pipeline for precision
Adjust retrieval settings, add reranking, or introduce Agentic RAG for multi-hop reasoning. Orq.ai lets you fine-tune every step to keep responses contextually accurate and domain-aligned.
Retrieval tuning
Tune your RAG pipeline for precision
Adjust retrieval settings, add reranking, or introduce Agentic RAG for multi-hop reasoning. Orq.ai lets you fine-tune every step to keep responses contextually accurate and domain-aligned.
Retrieval tuning
Tune your RAG pipeline for precision
Adjust retrieval settings, add reranking, or introduce Agentic RAG for multi-hop reasoning. Orq.ai lets you fine-tune every step to keep responses contextually accurate and domain-aligned.
Retrieval tuning
Tune your RAG pipeline for precision
Adjust retrieval settings, add reranking, or introduce Agentic RAG for multi-hop reasoning. Orq.ai lets you fine-tune every step to keep responses contextually accurate and domain-aligned.
RAG Evaluation
Measure how well your RAG truly performs
Run RAG-specific evaluations from groundedness checks to relevance scoring so you know exactly how your retrieval pipeline behaves before and after deployment.
RAG Evaluation
Measure how well your RAG truly performs
Run RAG-specific evaluations from groundedness checks to relevance scoring so you know exactly how your retrieval pipeline behaves before and after deployment.
RAG Evaluation
Measure how well your RAG truly performs
Run RAG-specific evaluations from groundedness checks to relevance scoring so you know exactly how your retrieval pipeline behaves before and after deployment.
RAG Evaluation
Measure how well your RAG truly performs
Run RAG-specific evaluations from groundedness checks to relevance scoring so you know exactly how your retrieval pipeline behaves before and after deployment.
Relevance scoring
RAG evals
Groundedness
online evaluators
analytics
Observability
Retrieval Observability
See how your knowledge base performs in real time
Track retrieval quality, latency, drift, and usage patterns from a clear dashboard. Spot stale data, diagnose failures, and keep your RAG system healthy without manual checks.
Retrieval Observability
See how your knowledge base performs in real time
Track retrieval quality, latency, drift, and usage patterns from a clear dashboard. Spot stale data, diagnose failures, and keep your RAG system healthy without manual checks.
Retrieval Observability
See how your knowledge base performs in real time
Track retrieval quality, latency, drift, and usage patterns from a clear dashboard. Spot stale data, diagnose failures, and keep your RAG system healthy without manual checks.
Retrieval Observability
See how your knowledge base performs in real time
Track retrieval quality, latency, drift, and usage patterns from a clear dashboard. Spot stale data, diagnose failures, and keep your RAG system healthy without manual checks.
Source attribution
Chunk-level traceability
Retrieval Transparency
Show the exact sources behind every answer
Orq.ai’s retrieval APIs return clean, structured citations with every response. Give users and teams confidence by showing where the answer came from - down to the chunk level.
Retrieval Transparency
Show the exact sources behind every answer
Orq.ai’s retrieval APIs return clean, structured citations with every response. Give users and teams confidence by showing where the answer came from - down to the chunk level.
Retrieval Transparency
Show the exact sources behind every answer
Orq.ai’s retrieval APIs return clean, structured citations with every response. Give users and teams confidence by showing where the answer came from - down to the chunk level.
Retrieval Transparency
Show the exact sources behind every answer
Orq.ai’s retrieval APIs return clean, structured citations with every response. Give users and teams confidence by showing where the answer came from - down to the chunk level.
External sources
API-first
Dynamic KB
Live Data Access
Extend your knowledge beyond your uploads
Connect to external knowledge bases and pull in live data. Retrieve the latest information on demand without rebuilding indexes.
Live Data Access
Extend your knowledge beyond your uploads
Connect to external knowledge bases and pull in live data. Retrieve the latest information on demand without rebuilding indexes.
Live Data Access
Extend your knowledge beyond your uploads
Connect to external knowledge bases and pull in live data. Retrieve the latest information on demand without rebuilding indexes.
Live Data Access
Extend your knowledge beyond your uploads
Connect to external knowledge bases and pull in live data. Retrieve the latest information on demand without rebuilding indexes.
Platform Solutions
Discover more solutions to build reliable AI products
Platform Solutions
Discover more solutions to build reliable AI products
Platform Solutions
Discover more solutions to build reliable AI products
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Why teams chose us
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
FAQ
Frequently asked questions
What is RAG as a Service?
RAG as a Service (Retrieval-Augmented Generation) is a managed solution that connects Large Language Models (LLMs) to your private data sources using optimized retrieval pipelines. Instead of building and maintaining complex infrastructure, teams can use pre-built tools to ingest unstructured data, configure retrieval logic, and generate context-aware, accurate responses. Orq.ai handles the orchestration, infrastructure, and performance tuning out of the box.
Why use RAG instead of fine-tuning an LLM?
RAG provides a flexible, scalable way to extend LLMs with domain-specific knowledge without retraining or fine-tuning the model itself. It ensures your GenAI systems stay current with real-time data while reducing cost, complexity, and model drift. You can update your retrieval layer or knowledge base without touching the underlying LLM.
What types of data can I connect to a RAG pipeline?
You can ingest a wide range of unstructured and semi-structured data formats including PDFs, emails, tables, images, HTML, and raw text. Orq.ai supports file upload through the UI or API, with built-in OCR and metadata indexing to make content fully searchable and retrievable in RAG workflows.
How does Orq.ai help optimize RAG performance?
Orq.ai provides tools to customize embeddings, chunking strategies, and retrieval logic, including keyword, vector, and hybrid search. You can also run structured RAG evaluations with built-in tracing and debugging tools to identify hallucinations or retrieval gaps, ensuring your system generates accurate, grounded responses.
Can I use Orq.ai’s RAG pipelines in production?
Yes. Orq.ai’s RAG pipelines are production-ready with enterprise-grade reliability, monitoring, and observability. You can integrate RAG workflows into live GenAI applications or agents, monitor performance in real time, and continuously improve results with minimal infrastructure overhead.
What is RAG as a Service?
RAG as a Service (Retrieval-Augmented Generation) is a managed solution that connects Large Language Models (LLMs) to your private data sources using optimized retrieval pipelines. Instead of building and maintaining complex infrastructure, teams can use pre-built tools to ingest unstructured data, configure retrieval logic, and generate context-aware, accurate responses. Orq.ai handles the orchestration, infrastructure, and performance tuning out of the box.
Why use RAG instead of fine-tuning an LLM?
RAG provides a flexible, scalable way to extend LLMs with domain-specific knowledge without retraining or fine-tuning the model itself. It ensures your GenAI systems stay current with real-time data while reducing cost, complexity, and model drift. You can update your retrieval layer or knowledge base without touching the underlying LLM.
What types of data can I connect to a RAG pipeline?
You can ingest a wide range of unstructured and semi-structured data formats including PDFs, emails, tables, images, HTML, and raw text. Orq.ai supports file upload through the UI or API, with built-in OCR and metadata indexing to make content fully searchable and retrievable in RAG workflows.
How does Orq.ai help optimize RAG performance?
Orq.ai provides tools to customize embeddings, chunking strategies, and retrieval logic, including keyword, vector, and hybrid search. You can also run structured RAG evaluations with built-in tracing and debugging tools to identify hallucinations or retrieval gaps, ensuring your system generates accurate, grounded responses.
Can I use Orq.ai’s RAG pipelines in production?
Yes. Orq.ai’s RAG pipelines are production-ready with enterprise-grade reliability, monitoring, and observability. You can integrate RAG workflows into live GenAI applications or agents, monitor performance in real time, and continuously improve results with minimal infrastructure overhead.
What is RAG as a Service?
RAG as a Service (Retrieval-Augmented Generation) is a managed solution that connects Large Language Models (LLMs) to your private data sources using optimized retrieval pipelines. Instead of building and maintaining complex infrastructure, teams can use pre-built tools to ingest unstructured data, configure retrieval logic, and generate context-aware, accurate responses. Orq.ai handles the orchestration, infrastructure, and performance tuning out of the box.
Why use RAG instead of fine-tuning an LLM?
RAG provides a flexible, scalable way to extend LLMs with domain-specific knowledge without retraining or fine-tuning the model itself. It ensures your GenAI systems stay current with real-time data while reducing cost, complexity, and model drift. You can update your retrieval layer or knowledge base without touching the underlying LLM.
What types of data can I connect to a RAG pipeline?
You can ingest a wide range of unstructured and semi-structured data formats including PDFs, emails, tables, images, HTML, and raw text. Orq.ai supports file upload through the UI or API, with built-in OCR and metadata indexing to make content fully searchable and retrievable in RAG workflows.
How does Orq.ai help optimize RAG performance?
Orq.ai provides tools to customize embeddings, chunking strategies, and retrieval logic, including keyword, vector, and hybrid search. You can also run structured RAG evaluations with built-in tracing and debugging tools to identify hallucinations or retrieval gaps, ensuring your system generates accurate, grounded responses.
Can I use Orq.ai’s RAG pipelines in production?
Yes. Orq.ai’s RAG pipelines are production-ready with enterprise-grade reliability, monitoring, and observability. You can integrate RAG workflows into live GenAI applications or agents, monitor performance in real time, and continuously improve results with minimal infrastructure overhead.
Enterprise control tower for security, visibility, and team collaboration.
Enterprise control tower for security, visibility, and team collaboration.