Retrieval-Augmented Generation systems that ground AI answers in your actual documents, databases, and knowledge base — not generic training data.
If your AI assistant needs to answer from your content instead of guessing, this is the engine that makes that work.
Upload PDFs, manuals, or policies — ask questions in plain language, get answers grounded in the actual text.
Replace keyword search across your internal wikis and drives with semantic search that understands intent.
Power your support chatbot's answers from your real help docs and past resolved tickets.
Search contracts, policies, and regulatory documents with citation-backed answers.
Pull context from databases, APIs, and documents simultaneously.
Combine retrieval with action-taking agents — find the right info, then act on it.
Send us a sample document set and we'll show you a working retrieval demo against it.
Retrieval-Augmented Generation means the AI looks up relevant information from your data before answering, instead of relying only on what it learned during training. It's the difference between an AI that guesses and one that looks things up.
No. RAG is the retrieval engine — it can power a chatbot, but it can also feed search results into a dashboard, a support tool, or an internal app. See AI Chatbot Development if you specifically want a conversational interface on top.
There's no hard minimum — even a few hundred pages of documentation can produce a useful system. What matters more is that the content is organized and accurate, since the system retrieves from what you give it.
Yes. We build ingestion pipelines for common formats and common platforms, including scanned/OCR'd documents where needed.
The index is re-synced on a schedule (or in real time, depending on setup) so the retrieval layer reflects your latest documents without retraining any model.
Depends on data volume, source complexity, and integration needs. Get a free estimate based on your actual use case.
Yes — ongoing index maintenance, retrieval quality tuning, and monitoring so answer quality doesn't drift as your data grows.