What is RAG and why does it matter?

Retrieval-augmented generation searches your own content for relevant information before the AI generates an answer, so responses are grounded in facts you control instead of a model's training data alone.

Does this reduce hallucination?

It significantly reduces it, because the model answers from retrieved passages rather than from memory, and we configure it to say it doesn't know when nothing relevant is found.

Can it respect our existing access permissions?

Yes — we build retrieval to honor the same permissions your documents already have, so a search never surfaces something a user shouldn't see.

What kinds of content can it search?

PDFs, wikis, ticketing systems, spreadsheets, internal databases and more — anything that can be indexed can be made retrievable.

How do you keep the knowledge base current?

We set up automatic re-indexing on a schedule or on content change, so answers reflect your latest documents.

AI Solutions

Answers Grounded in Your Own Data

We build retrieval-augmented generation systems so AI answers come from your documents and databases — not a model's guesswork — with sources you can verify.

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Generic AI doesn't know your business

A general-purpose model has never seen your internal wiki, contracts or product specs — so it guesses, and sometimes guesses wrong. We build retrieval-augmented generation pipelines that search your own content first, then generate answers grounded in what was actually found, with citations back to the source document.

What we build

Document & data ingestion

Index PDFs, wikis, databases and internal tools into a searchable knowledge base.

Semantic search

Vector search that finds relevant passages by meaning, not just keywords.

Grounded generation

Answers are generated from retrieved content, reducing hallucination.

Source citations

Every answer links back to the document or record it came from.

Access-aware retrieval

Search results respect existing permissions, so people only see what they're allowed to.

How we build it

Step 1

Audit your content

We catalog the documents, wikis and databases worth making searchable.

Step 2

Build the index

We chunk, embed and index content into a retrieval-ready knowledge base.

Step 3

Tune retrieval quality

We test and refine search relevance against real questions your team asks.

Step 4

Connect generation

We wire retrieved context into the language model with citation tracking.

Step 5

Deploy & evaluate

We launch with evaluation metrics in place to catch drift over time.

Frequently asked questions

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