Technology
RAG Systems
Build AI applications that combine language models with your data. RAG systems retrieve relevant context from documents and use it to generate accurate, informed responses.
Best For
Ideal use cases
Chatbots needing company-specific knowledge
Question-answering systems
Applications requiring factual accuracy
Knowledge bases with AI search
Customer support automation
What We Build
Projects we deliver
Knowledge base chatbots
Document Q&A systems
Customer support assistants
Internal knowledge search
Research assistants
Educational platforms
Legal document analysis
Technical documentation assistants
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
Customer support chatbots with product knowledge
Internal knowledge bases with AI search
Educational platforms with Q&A
Research tools analyzing documents
Applications needing accurate, sourced responses
Delivery
How we deliver
We design RAG systems with proper document chunking and indexing
Implement retrieval strategies balancing relevance and diversity
Use prompt engineering to incorporate retrieved context
Set up evaluation metrics and monitoring
Implement feedback loops for continuous improvement
FAQ
Frequently asked questions
RAG systems retrieve relevant documents and use them as context for language models. This provides factual, up-to-date information and reduces hallucinations compared to models relying only on training data.
We can use PDFs, Word documents, Markdown files, web pages, and other text formats. We parse and chunk documents appropriately, and can handle structured data like databases or APIs.
We evaluate RAG systems using metrics like retrieval accuracy, answer relevance, and user feedback. We test with sample queries, monitor response quality, and iterate based on results.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus a fixed-scope gig to start quickly.
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