Retrieval-augmented generation for AI answers based on your own data.
Retrieval-augmented generation connects a language model with your own content, instead of relying on what the model happens to know on its own. Before it answers, it searches your documents for the relevant passages and grounds its response in them. This keeps the statements current, because you never retrain a model, you simply maintain your knowledge base. And because every answer rests on concrete sources, it can be backed up and the inventing of facts becomes far rarer.
We build RAG when an assistant needs to know about your internal content, such as manuals, support articles or product data. Instead of forcing the knowledge into the model, we keep it in a searchable source that you update at any time. That gives you answers that fit your company and can be traced back to the passage they came from.
Good to know
Quality stands or falls with chunking, namely how you split documents before indexing. Chunks that are too large dilute relevance, ones that are too small tear the meaning apart; also attach each passage's source as metadata, so you can cite cleanly at the end.
More tools we work with in the same area.
OpenAI
GPT models for text, analysis and intelligent features.
Anthropic
Claude models for secure, high-performance AI integrations.
Vector Databases
Semantic search and knowledge bases for AI applications.
LangChain
A framework for orchestrating LLM workflows, agents and tools.
Hugging Face
Open-source models and inference for AI that fits you exactly.
MCP
Model Context Protocol for a clean connection between AI and your tools.
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