Service · 09

RAG Development & Enterprise Search

Build retrieval-augmented generation (RAG) systems that answer from your own documents.

The problem

A search returns six versions of the same policy. Staff need the current passage and a link they can verify before advising a customer.

Our approach

We index approved sources, retain access rules, and test retrieval against questions your team actually asks. The assistant cites the passages it used or flags when the sources do not support an answer.

What's included

What we handle for you.

Source indexing

Track document versions, removals, and access changes when the index refreshes.

Retrieval testing

Check whether the correct passage appears for real questions, including ones the system should decline.

Deliverables

What you walk away with.

A searchable index connected to approved sources
A citation and retrieval quality report

Ideal for

Is this you?

Teams with large policy, product, or technical libraries

Talk to us about this

Ongoing AI Support

Available with this service after launch.

This is not a separate service. It is the ongoing support option for keeping RAG Development & Enterprise Search useful, maintained, and improving as your business changes.

Monitoring and small improvements after launch

New automation, dashboard, or workflow updates as the business changes

Monthly review of what is working, what is stuck, and what to build next

Support for connected CRM, data, email, and reporting workflows

In practice

Keep answers current when policies change

Retrieval-augmented generation finds relevant passages before drafting an answer. A pilot should test an updated policy, a deleted document, and a question that the library cannot answer.

Bring representative questions and the source passages you expect to see.

Review your document search →

Frequently asked questions

How is RAG different from model training?

RAG retrieves material when a question is asked. Updating the searchable collection can change the supporting evidence without retraining the model.

How do you evaluate retrieval quality?

Build a question set with expected sources, then check passage relevance, citation accuracy, and whether unsupported questions are declined.

Which documents should go into the first index?

Start with a curated collection containing answers to your test questions. Identify the owner, current version, and access rules before adding more sources.

Can RAG search tables and scanned files?

It can when extraction preserves their meaning. Test representative pages, especially tables where column headers, units, and footnotes change the answer.

What happens when a source is deleted?

The index needs a deletion path as well as an import path. Verify that the removed material stops appearing in search results and citations.

Can we reuse an existing search index?

Potentially. We test relevance, metadata, permissions, and update behavior first. A useful existing index may only need a retrieval and citation layer.

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