RAG Search Intelligence
The Challenge
Traditional search returns hundreds of documents when you need one specific answer. Teams lose hours digging through files, asking whoever might remember, or recreating information that already exists somewhere. The knowledge is in the building. Nobody can reach it fast enough to matter.
Built and Running at TMG
This page describes a system The McBurney Group runs every day, on its own operations. TMG's RAG deployment indexes 21,000+ documents across 7 sources into one searchable knowledge base. Ask a question in plain language and get an answer in 2 to 5 seconds, with citations back to the source documents. The whole thing is reachable from a single Slack command, so answers arrive where the work already happens.
How It Works
RAG (Retrieval-Augmented Generation) understands the question's context and retrieves precise answers from your knowledge base instead of a list of maybe-relevant files. Answers cite specific sources with direct links, so trust is checkable, and the system synthesizes across documents when the answer lives in more than one place.
Key Features
- Natural language querying across all document types
- Source attribution with direct links to originals
- Multi-document synthesis for questions no single file answers
- Delivery inside the tools your team already uses
- Integration with existing document management systems
Impact
Measured on TMG's own deployment: research that used to mean digging through folders now resolves in seconds, and new material becomes findable the day it's indexed. A version built on your documents behaves the same way, sized to your sources and your team's tools.
Common Questions
How is RAG search different from a regular search engine?
A regular search returns a list of documents that might contain your answer. RAG reads those documents, understands the question context, and returns a direct answer with citations to the source material. You get an answer, not a reading list.
What types of documents can the system index?
The system processes PDFs, Word documents, spreadsheets, presentations, markdown files, wiki pages, and plain text. If your organization produces it, the system can index and search it. New documents become searchable the day they are indexed.
How accurate are the answers?
Every answer includes citations linking back to the specific source documents. If the system cannot find a confident answer, it says so rather than guessing. The citation model means trust is checkable: you can verify any answer by following the link to the original material.
30-Minute Discovery Session
We'll map your biggest knowledge search bottlenecks and tell you whether automation fixes them or your process needs to change first.