Agents with RAG
We build AI agents with RAG that search your company’s knowledge base, including documents, manuals, wikis, and contracts, and answer accurately while citing the source of each piece of information. For companies that want to use AI with their own data without exposing it to third-party model training.
Delivered by Vitamina Digital, a VitaminaWeb group company.
What you get
- 01Ingestion of documents in PDF, Word, HTML, and other text formats
- 02Semantic search with vector embeddings
- 03Answers that cite the source document
- 04Incremental updates to the knowledge base
- 05Support for multiple data sources
- 06Data stays in your environment and is not used to train external models
How it works
- Curation
We select and organize the documents that make up the knowledge base.
- Pipeline
We set up ingestion, embeddings, semantic search, and integration with the LLM.
- Fine-tuning
We calibrate prompts and retrieval to deliver accurate answers with citations.
- Evaluation
We measure answer quality with automated tests before deployment.
When it makes sense
The answers are in our documents, but no one can find them in time.
We tried generic AI, and it made up answers about the company.
We need the AI to show where it got each piece of information.
Our data is sensitive and cannot be used to train third-party models.
Frequently asked questions
What is RAG?
RAG stands for Retrieval-Augmented Generation. Before generating an answer, the system searches your knowledge base for relevant passages and uses them as context for the LLM, producing answers based on your data.
Which documents can I use?
PDF, Word, Excel, HTML, Markdown, plain text, and emails, so virtually any text-based document. For images and videos, we use multimodal models when it makes sense.
Will my data stay secure?
Yes. Embeddings are stored in a private vector database, and the original documents remain in your environment. Nothing is used to train third-party models.
How can I tell if the answers are correct?
Each answer identifies the document it came from, making it verifiable. We also measure precision and recall with automated tests to check whether answers are correct, not just fluent.
How much does it cost, and how long does it take?
It depends on the volume and organization of the documents and the sources to be integrated. The timeline and investment are defined in the proposal after we assess the knowledge base.