Build AI that knows your data. RAG combines LLMs with your documents for accurate, grounded responses with source citations.
Standard LLMs only know what they were trained on. RAG lets AI access your specific knowledge in real-time.
AI answers based on your actual data, not just model knowledge. Reduce hallucinations significantly.
Find information by meaning, not just keywords. Understand context and intent in queries.
Update your knowledge base instantly. No model retraining needed for new information.
Every answer includes references to source documents. Full traceability and transparency.
Your data stays in your infrastructure. Self-hosted options for sensitive information.
Use smaller models with RAG for better results at lower cost than fine-tuning.
From enterprise knowledge bases to customer-facing chatbots, RAG powers intelligent information access.
AI assistant that answers questions about your internal documents, policies, and procedures.
Chatbot that resolves tickets using your product documentation and past support cases.
Search and analyze contracts, regulations, and legal documents with AI.
AI that helps researchers find and synthesize information from large document collections.
Conversational commerce with AI that knows your entire product catalog.
Developer assistant that answers questions about APIs, codebases, and technical specs.
A production-ready RAG system involves multiple components working together for optimal retrieval and generation.
We process your documents (PDFs, Word, HTML, databases) and extract clean, structured text with metadata preservation.
Documents are split into optimal chunks with overlap. We use semantic chunking to preserve context and meaning.
Each chunk is converted to a vector embedding that captures its semantic meaning for similarity search.
Embeddings are stored in a vector database optimized for fast similarity search at scale.
When a query comes in, we find the most relevant chunks using hybrid search (semantic + keyword).
Retrieved context is combined with the query in a prompt, and the LLM generates a grounded response.
We select the right vector database based on your scale, performance, and operational requirements.
Simplicity, scale, serverless
Hybrid search, GraphQL API
Performance, filtering, Rust
Local development, simple
PostgreSQL integration
Large scale, GPU acceleration
A structured approach to building RAG systems that scale and deliver accurate results.
We analyze your documents, data sources, and use cases to design the optimal RAG architecture.
Build automated pipelines to ingest, process, and embed your documents into the vector store.
Implement and tune retrieval strategies for maximum relevance and accuracy.
Connect retrieval to LLM with optimized prompts and response formatting.
Production deployment with monitoring, feedback collection, and continuous improvement.
RAG (Retrieval-Augmented Generation) combines LLMs with your own data. Instead of relying only on what the model was trained on, RAG retrieves relevant information from your documents and includes it in the prompt. You need RAG if you want AI to answer questions about your specific content—company knowledge bases, product catalogs, legal documents, or any proprietary information.
RAG grounds LLM responses in actual source documents. When the AI answers, it's working from retrieved text, not just generating from its training data. We also implement citation requirements so every claim references a source, confidence scoring to flag uncertain answers, and fallback responses when relevant context isn't found.
RAG can process virtually any text-based content: PDFs, Word documents, web pages, emails, Slack messages, Notion pages, Confluence wikis, database records, API responses, and more. We can also handle images and tables with OCR and specialized parsing. The key is building the right ingestion pipeline for your data sources.
It depends on your requirements. Pinecone is great for managed simplicity and serverless scale. Weaviate offers excellent hybrid search and a GraphQL API. Qdrant provides high performance with advanced filtering. pgvector is ideal if you're already using PostgreSQL. We help you choose based on scale, latency, cost, and operational requirements.
RAG project costs vary depending on complexity, data sources, and scale requirements. A basic RAG chatbot with simple document processing requires less investment, while enterprise systems with multiple data sources, advanced retrieval, and complex integrations require more. We provide detailed quotes after understanding your specific requirements during the discovery phase.
A basic RAG system can be built in 6-8 weeks. Enterprise RAG with multiple data sources, custom processing, and production-grade infrastructure takes 3-5 months. The timeline depends heavily on data complexity—clean, structured documents are faster than messy, varied sources requiring custom parsing.
Yes. We offer self-hosted RAG solutions where your data never leaves your infrastructure. This includes self-hosted vector databases (Qdrant, Weaviate, pgvector), self-hosted LLMs (Llama 3, Mistral), and encrypted data pipelines. For regulated industries like healthcare and finance, we implement compliance-ready architectures.
We use multilingual embedding models that work across languages. This means a query in English can find relevant documents in Spanish, French, or other languages. We can also implement language-specific pipelines when needed, with translation and language detection for optimal results.
Tell us about your data and use case. We'll design a RAG architecture that delivers accurate, grounded AI responses.
Fill out the form below and our team will get back to you within 24 hours with a personalized proposal for your project.
Stop relying on generic AI. Build a RAG system that delivers accurate, grounded responses from your own knowledge base.
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