Retrieval-augmented generation over your documents, tickets and records. We build the ingestion, embeddings and vector search that let AI answer from your knowledge and show where each answer came from.
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Answers live in PDFs, wikis, tickets and databases. Keyword search misses them, and a plain LLM fills the gaps with confident guesses.
Good retrieval is the difference between a helpful assistant and a confident liar
Responses come from your content, not the model’s guesses.
Every answer links to its sources so people can verify it.
Embeddings find relevant passages even when the wording differs.
Incremental ingestion keeps the index in step with your content.
We pull content from your sources, clean it, and split it into chunks that keep their meaning and metadata.
Embeddings go into a vector database, combined with keyword search and reranking, tuned on questions your users actually ask.
The model answers only from retrieved context, cites sources, and says when it does not know. Evals track retrieval and answer quality over time.
Chosen to fit your data, scale and budget
Inventory sources, formats, owners and access rules.
Collect real questions with known answers to measure against.
Build connectors, cleaning, chunking and embeddings.
Tune chunk size, hybrid search and reranking on the question set.
Add grounded generation with citations and fallbacks.
Ship to users, then track quality, gaps and cost.
Built into every RAG pipeline we deliver
Share where your knowledge lives today. We will propose a RAG pipeline that answers your team’s real questions, with sources.