RAG That
Actually Delivers
Knowledge retrieval, fine-tuned for your specific domain.
Get accurate, trusted, and contextually aware AI.
Get accurate, trusted, and contextually aware AI. Built with domain-specific ontologies and generative pipelines.
OpenAI Large
Foundation Reranker
BGE
Foundation Reranker
K²
+35%
more accurate than general embedding models from leading AI labs
+50%
Less token overhead leading to faster generation & lower cost
Less input token overhead leading to faster generation & lower cost
100%
customizable for your
use-case, trained on your data, in your environment
customizable for your use-case, in your environment
customizable for your use-case
Customized, Fine-Tuned RAG
Enterprise-grade RAG with precision-tuned embeddings, custom rerankers, and intelligent chunking. High performing, scalable retrieval across your private data.
Customized, Fine-Tuned RAG
Enterprise-grade RAG with precision-tuned embeddings, custom rerankers, and intelligent chunking. High performing, scalable retrieval across your private data.
Customized, Fine-Tuned RAG
Enterprise-grade RAG with precision-tuned embeddings, custom rerankers, and intelligent chunking. High performing, scalable retrieval across your private data.
Launch Production-Ready AI
Enterprise-grade RAG with precision-tuned embeddings, custom rerankers, and intelligent chunking. High performing, scalable retrieval across your private data.
Evaluation, Monitoring, and Optimization
Real-time telemetry and tracing across your RAG stack. Detect drift, uncover regressions, and continuously tune performance — automatically.
Evaluation, Monitoring, and Optimization
Real-time telemetry and tracing across your RAG stack. Detect drift, uncover regressions, and continuously tune performance — automatically.
Evaluate, Monitor, Optimize.
Real-time telemetry and tracing across your RAG stack. Detect drift, uncover regressions, and continuously tune performance — automatically.
Evaluation, Monitoring, and Optimization
Real-time telemetry and tracing across your RAG stack. Detect drift, uncover regressions, and continuously tune performance — automatically.
Available Where You Are
Use our managed APIs or run in your own cloud–AWS, Google Cloud, or Azure. Your data, your environment, your control.
Available Where You Are
Use our managed APIs or run in your own cloud–AWS, Google Cloud, or Azure. Your data, your environment, your control.
Available Where You Are
Use our managed APIs or run in your own cloud–AWS, Google Cloud, or Azure. Your data, your environment, your control.
Available Where You Are
Use our managed APIs or run in your own cloud–AWS, Google Cloud, or Azure. Your data, your environment, your control.
Deliver Trusted Results
Confidently deliver accurate, reliable information every time. Advanced reranking minimizes hallucinations, ensuring trust in customer-facing and compliance-critical AI applications.
Deliver Trusted Results
Confidently deliver accurate, reliable information every time. Advanced reranking minimizes hallucinations, ensuring trust in customer-facing and compliance-critical AI applications.
Deliver Trusted Results
Confidently deliver accurate, reliable information every time. Advanced reranking minimizes hallucinations, ensuring trust in customer-facing and compliance-critical AI applications.
Deliver Trusted Results
Confidently deliver accurate, reliable information every time. Advanced reranking minimizes hallucinations, ensuring trust in customer-facing and compliance-critical AI applications.
Grounded in Your Data
Ensure AI responses stay accurate, relevant, and up-to-date. Domain-aware retrieval dynamically adapts to evolving documents, policies, and internal expertise.
Grounded in Your Data
Ensure AI responses stay accurate, relevant, and up-to-date. Domain-aware retrieval dynamically adapts to evolving documents, policies, and internal expertise.
Grounded in Your Data
Ensure AI responses stay accurate, relevant, and up-to-date. Domain-aware retrieval dynamically adapts to evolving documents, policies, and internal expertise.
Grounded in Your Data
Ensure AI responses stay accurate, relevant, and up-to-date. Domain-aware retrieval dynamically adapts to evolving documents, policies, and internal expertise.
Scale Without Drift
Maintain accuracy and consistency at any scale. Built-in evaluation and automated tuning ensure your RAG continuously improves, even as your data and usage grow.
Scale Without Drift
Maintain accuracy and consistency at any scale. Built-in evaluation and automated tuning ensure your RAG continuously improves, even as your data and usage grow.
Scale Without Drift
Maintain accuracy and consistency at any scale. Built-in evaluation and automated tuning ensure your RAG continuously improves, even as your data and usage grow.
Scale Without Drift
Maintain accuracy and consistency at any scale. Built-in evaluation and automated tuning ensure your RAG continuously improves, even as your data and usage grow.
Frequently Asked Questions
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI workflow that retrieves relevant information (e.g., documents or chunks of text) from a knowledge source and feeds it into a Large Language Model (LLM) to help generate more accurate, context-rich answers. Instead of relying solely on the LLM’s internal parameters, RAG “injects” the latest or domain-specific knowledge to reduce hallucinations and improve trustworthiness.
Why do standard RAG solutions often fail for domain-specific queries?
What makes embedding fine-tuning so critical, and how does K² handle it?
How does advanced re-ranking help, and why is it so important?
How do you measure success and maintain performance as data evolves?
Is RAG the long-term future of enterprise AI, and how does K² stay future-proof?
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI workflow that retrieves relevant information (e.g., documents or chunks of text) from a knowledge source and feeds it into a Large Language Model (LLM) to help generate more accurate, context-rich answers. Instead of relying solely on the LLM’s internal parameters, RAG “injects” the latest or domain-specific knowledge to reduce hallucinations and improve trustworthiness.
Why do standard RAG solutions often fail for domain-specific queries?
What makes embedding fine-tuning so critical, and how does K² handle it?
How does advanced re-ranking help, and why is it so important?
How do you measure success and maintain performance as data evolves?
Is RAG the long-term future of enterprise AI, and how does K² stay future-proof?
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI workflow that retrieves relevant information (e.g., documents or chunks of text) from a knowledge source and feeds it into a Large Language Model (LLM) to help generate more accurate, context-rich answers. Instead of relying solely on the LLM’s internal parameters, RAG “injects” the latest or domain-specific knowledge to reduce hallucinations and improve trustworthiness.
Why do standard RAG solutions often fail for domain-specific queries?
What makes embedding fine-tuning so critical, and how does K² handle it?
How does advanced re-ranking help, and why is it so important?
How do you measure success and maintain performance as data evolves?
Is RAG the long-term future of enterprise AI, and how does K² stay future-proof?
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI workflow that retrieves relevant information (e.g., documents or chunks of text) from a knowledge source and feeds it into a Large Language Model (LLM) to help generate more accurate, context-rich answers. Instead of relying solely on the LLM’s internal parameters, RAG “injects” the latest or domain-specific knowledge to reduce hallucinations and improve trustworthiness.