Unihox builds enterprise Retrieval-Augmented Generation (RAG) systems that combine the power of LLMs with your organization's knowledge. Create intelligent Q&A systems, document search, and knowledge bases that deliver accurate, sourced answers.
RAG (Retrieval-Augmented Generation) is an AI architecture that enhances Large Language Models by giving them access to external knowledge sources. Instead of relying solely on training data, RAG systems retrieve relevant documents from your knowledge base and use them to generate accurate, contextual, and up-to-date responses.
This approach solves key LLM limitations: hallucinations (making up facts), outdated information (knowledge cutoff), and lack of domain expertise. RAG enables your AI to answer questions about your specific documents, products, policies, and internal knowledge.
Build conversational interfaces over your documents. Users ask questions in natural language and get accurate answers with source citations.
Create searchable knowledge repositories that understand context, not just keywords. Perfect for internal wikis and documentation.
Deploy AI assistants that answer customer queries using your product docs, FAQs, and support history.
Search through contracts, regulations, and legal documents with AI that understands legal terminology and context.
Analyze large document collections, research papers, and reports to extract insights and answer complex questions.
RAG systems that work with images, tables, charts, and text for comprehensive document understanding.
Pinecone
Managed Vector DB
Weaviate
Open Source Vector DB
ChromaDB
Lightweight Vector DB
Qdrant
High-Performance DB
pgvector
PostgreSQL Extension
LangChain
LLM Framework
LlamaIndex
Data Framework
OpenAI Embeddings
Embedding Model
RAG (Retrieval-Augmented Generation) is an AI architecture that enhances LLM responses by retrieving relevant information from external knowledge sources before generating answers. Instead of relying solely on the model's training data, RAG systems search through your documents, databases, or knowledge bases to find relevant context, then use this information to generate accurate, up-to-date, and grounded responses.
Use RAG when: (1) Your data changes frequently, (2) You need to cite sources, (3) You have large document collections, (4) You need real-time information. Use fine-tuning when: (1) You need the model to learn a specific style or tone, (2) You have stable, unchanging domain knowledge, (3) You need faster inference without retrieval latency. Many enterprise solutions combine both approaches.
Unihox has expertise in all major vector databases: Pinecone (managed, scalable), Weaviate (open-source, feature-rich), ChromaDB (lightweight, local), Qdrant (open-source, performant), Milvus (enterprise-grade), PostgreSQL with pgvector (SQL-compatible), and Elasticsearch with vector search. We help you choose based on scale, cost, and requirements.
RAG development costs depend on complexity and scale. Basic RAG implementations for document Q&A start at $10,000-25,000. Enterprise RAG systems with multiple data sources, hybrid search, and advanced features range from $30,000-100,000. Large-scale production systems with millions of documents can cost $100,000+. Contact us for a detailed estimate.
We implement multiple strategies: (1) High-quality document chunking and embedding, (2) Hybrid search combining semantic and keyword search, (3) Reranking retrieved documents for relevance, (4) Prompt engineering to ground responses in retrieved context, (5) Confidence scoring and source attribution, (6) Guardrails to detect and prevent fabricated responses.
Yes, Unihox builds secure RAG systems for sensitive data. Options include: (1) On-premise deployment with local LLMs (Llama, Mistral), (2) Private cloud with VPC isolation, (3) Encrypted vector storage, (4) Role-based access control for documents, (5) Audit logging for compliance. We help enterprises meet GDPR, HIPAA, and SOC 2 requirements.
Transform your documents into an intelligent knowledge base. Get a free consultation to discuss your RAG implementation.
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