SEARCH YEARS OF INTERNAL KNOWLEDGE IN SECONDS
Private document search uses RAG (Retrieval-Augmented Generation) to let your staff ask questions across thousands of internal documents and get answers with source citations — all processed on your own hardware. We deploy this for Melbourne firms that need fast knowledge retrieval across policies, matter files, procedures, and operational records without sending anything to public AI platforms.
Most firms have years of institutional knowledge locked in shared drives, intranets, and filing systems that nobody can search effectively. A keyword search only works if you guess the right term. Semantic search understands what you're actually asking and finds the right paragraph in the right document — even if the wording doesn't match.
CAPABILITIES
HOW IT WORKS
DOCUMENT INGESTION
We pull documents from your shared drives, intranets, or document management systems. PDFs, Word files, Excel spreadsheets, plain text — the system handles the formats your firm actually uses. Everything is processed and stored locally.
SEMANTIC INDEXING
Documents are split into passages and converted into vector embeddings using models like BGE or E5. These embeddings capture meaning, not just keywords, and are stored in a vector database (ChromaDB or Qdrant) running on your server.
PRIVATE QUERY PROCESSING
Staff type a question in plain English. The system finds the most relevant passages from your document set, feeds them to a local language model, and returns an answer with citations to the source documents — all processed on your hardware, nothing sent externally.
Firms sitting on thousands of documents that nobody can search properly. Law firms with decades of precedents buried in shared drives. Healthcare providers with clinical guidelines scattered across intranets. Financial services teams with years of compliance correspondence in nested folder structures. If your people are spending hours finding documents that should take seconds, this is what you need.
"People obsess over which language model to use, but for document search, the real quality bottleneck is the embedding and chunking pipeline. How you split documents into passages, which embedding model you use, and how you tune the retrieval threshold — that's what determines whether the system returns the right paragraph from the right document. Get the retrieval right and even a smaller model gives excellent answers."
FREQUENTLY ASKED QUESTIONS
How does private document search work?
It uses a technique called RAG (Retrieval-Augmented Generation). First, we ingest your documents and convert them into vector embeddings using models like BGE or E5 — this captures the meaning of each passage, not just the keywords. These embeddings are stored in a vector database (such as ChromaDB or Qdrant) running on your server. When someone asks a question, the system finds the most relevant passages, feeds them to a local language model, and generates an answer with citations to the source documents. Everything runs on your hardware — no data leaves your network.
What types of documents can be searched?
We index policies, procedures, matter files, reports, knowledge bases, contracts, correspondence, and operational records. The system handles PDF, Word, Excel, and plain text — the formats most Australian firms actually use. We've indexed collections ranging from a few hundred documents to over 50,000, and the retrieval quality scales well across that range. If your documents are in a shared drive or intranet, we can usually ingest them as-is.
How accurate is AI powered document search compared to keyword search?
Semantic search understands meaning, not just words. If someone asks 'What's our policy on keeping client records after a matter closes?' the system finds the relevant retention policy — even if the document never uses the exact phrase 'keeping client records.' Traditional keyword search would miss that entirely. In our deployments, staff consistently find documents they didn't know existed, because the old keyword approach only worked if you guessed the right term. The accuracy gap is most obvious with large, legacy document sets where naming conventions are inconsistent.
Want to See How This Works for Your Firm?
We'll walk you through a deployment that fits your setup — your documents, your infrastructure, your compliance requirements. No sales pitch.
Request a Consultation →Or email us directly at hello@airgapllm.com.au