RAG (Retrieval-Augmented Generation)
Make Your AI Smarter with Retrieval-Augmented Generation
RAG (Retrieval-Augmented Generation) enhances LLMs by combining generative AI with real-time, domain-specific data. This allows your AI system to provide more accurate, up-to-date, and context-aware answers.
We build custom RAG pipelines that combine embeddings, vector databases, and intelligent prompt engineering — helping you build smarter assistants, chatbots, and internal knowledge tools.
- Custom RAG Architecture Design
- Vector DB Integration (Pinecone, Weaviate, Qdrant)
- Embedding Models & Data Chunking
- Prompt Engineering & Context Injection
- RAG for Documents, PDFs, Websites & Databases
- Scalable Infrastructure for Production Use
Who is this for?
Enterprises building internal knowledge assistants.
Startups creating domain-specific AI apps.
Developers enhancing LLMs with contextual accuracy.
Frequently Asked Questions
RAG combines LLMs with a retrieval mechanism that fetches relevant data before generating responses, resulting in more accurate and contextually relevant answers.
Yes. We can ingest PDFs, docs, databases, website content, and more — then transform them into embeddings for real-time querying by the AI.
We support Pinecone, Weaviate, Qdrant, Milvus, and other scalable vector databases depending on your architecture and budget.
In many cases, yes. RAG doesn’t require retraining the model and offers flexibility, cost efficiency, and real-time access to updated knowledge.
Absolutely. We offer deployment and scaling solutions on AWS, Azure, or custom cloud infrastructure with monitoring and support.
Let’s Build Future Together.
