RAG Solutions

RAG (Retrieval-Augmented Generation) combines intelligent search with generative AI. The system retrieves the most relevant information from your knowledge base and uses an LLM to generate precise, contextualized answers, always grounded in your actual data.

Virtually any textual source: source code, technical documentation, wikis, support tickets, emails, internal processes, manuals, databases, and APIs. The system is flexible enough to accommodate multiple formats and sources simultaneously.

In most cases, RAG operates on source code and technical documentation, which don't constitute sensitive personal data. For scenarios involving regulated data, we implement granular access controls, encryption, and retention policies aligned with your compliance framework.

RAG complements and enhances existing documentation. Rather than replacing it, RAG makes all documentation - including outdated material - accessible and useful again, connecting information from multiple sources for more complete answers.

RAG works in three phases: first, it enables understanding legacy code before migrating; then, it evolves alongside the migration capturing decisions and changes; finally, it becomes a feature of the modernized system. The investment multiplies throughout the project.

A functional MVP can be delivered in a few weeks, depending on the volume and complexity of knowledge sources. The solution evolves iteratively, with each cycle expanding coverage and refining answer quality.

Yes. User feedback fuels continuous improvement cycles. The system learns which answers are most useful, refines result relevance, and adapts as new knowledge sources are added.