RAG & Document Intelligence
AI That Knows Your Business, Not Just the Internet
Retrieval-Augmented Generation connects your AI to your institutional knowledge — your contracts, policies, manuals, regulatory filings, and internal documentation. FI Digital builds production RAG pipelines using Claude, GPT-4o, and LangChain that retrieve the right context before answering, deciding, or acting. Accurate. Auditable. Deployed in Australian infrastructure.
The Problem With Vanilla AI
Off-the-shelf AI models know a great deal about the world as it was when they were trained. They do not know your contracts. They do not know your compliance policies. They do not know your internal procedures, your product catalogue, your pricing rules, or the regulatory requirements specific to your industry.
Without access to your institutional knowledge, AI gives you generic answers. Useful for some tasks. Dangerously inadequate for others.
Retrieval-Augmented Generation solves this. RAG is an architecture pattern that gives your AI agents access to your specific documents before they answer a question or make a decision. Instead of relying solely on training data, the agent retrieves relevant chunks from your document corpus and uses that retrieved context alongside the model's reasoning capability. The result is AI that answers with accuracy derived from your actual documents.
How a RAG Pipeline Works
Five precise layers — from your documents to a cited, auditable answer.
Document Sources
Your institutional knowledge — contracts, policies, regulatory filings, SharePoint libraries, email archives, technical manuals — is wired in as the authoritative source of truth. The AI never guesses; it reads your documents.
Ingestion & Vector Indexing
Documents are parsed, split into semantically coherent chunks, converted to vector embeddings, and stored in a vector database with rich metadata — enabling retrieval filtered by type, date, category, or business unit.
Semantic Retrieval
When a user asks a question, the query is embedded and compared to the vector index. The most relevant document chunks are retrieved instantly — scoped by role-based access so users only see what they're allowed to see.
LLM Reasoning
Retrieved chunks are passed as context to the reasoning model alongside the question. The model generates a grounded answer constrained to retrieved content — structurally eliminating hallucination. GPT-4o for multimodal tasks; Claude for long-context documents.
Answer + Audit Trail
The final answer arrives with citations — exact document, page, and paragraph. Every inference is logged: model input, output, confidence score, and user identity. Regulated industries get the audit trail they require.
What We Build
Document Ingestion & Vector Indexing
We build ingestion pipelines handling PDF, Word, Excel, SharePoint, and email sources. Documents are chunked using semantic strategies that preserve context. Each chunk is embedded and stored in a vector database — Pinecone, Weaviate, Azure AI Search, or pgvector. Metadata filtering enables retrieval scoped by document type, date, category, or business unit.
Knowledge Retrieval & Question Answering
Users ask questions in natural language. The pipeline retrieves the most relevant document chunks, passes them as context to Claude or GPT-4o, and returns an answer with citations to source documents. Hallucination is structurally minimised — the model is constrained to reason over retrieved content, not training data.
Document Classification & Extraction
Our document intelligence systems classify incoming documents and extract structured data from unstructured text. An AI agent that receives an email attachment identifies whether it is a contract, invoice, regulatory notice, or customer complaint — routes it, and extracts key fields into structured records. 95%+ extraction accuracy on standard document types.
Multi-Document Reasoning
Some use cases demand reasoning across multiple documents simultaneously. A compliance officer needs to know whether a proposed agreement is consistent with current policy and regulatory requirements — three different documents. Our multi-document RAG systems retrieve in parallel, synthesise results, and attribute sources across all input documents.
Governance, Accuracy, and Compliance
FI Digital has built production RAG pipelines that have processed millions of pages of contracts, regulatory filings, clinical guidelines, and technical manuals — with audit trails that regulated industries require.
Citation & Source Attribution
Every AI-generated answer includes references to the specific document, page, and paragraph.
Confidence Scoring
Low-confidence retrievals are flagged for human review rather than presented as authoritative answers.
Retrieval Quality Monitoring
Automated evaluation of retrieval precision and recall using a test question set drawn from your actual use cases.
Role-Based Access Control
Retrieval is scoped to documents the querying user is authorised to access — enforced at the retrieval layer.
Australian Data Residency
Your documents are stored and processed in Australian infrastructure. Your institutional knowledge does not leave your jurisdiction.
Vector Database Options
We select the vector database based on your existing infrastructure — not ours.
Frequently Asked Questions
Ready to connect AI to your documents?
Book a free RAG & Document Intelligence discovery session. We will scope your document corpus and show you what production-grade retrieval accuracy looks like on your actual content.