Enterprise RAG • AI Connected to Your Data

AI That Knows
Your Business.

Retrieval Augmented Generation connects AI directly to your documents, databases, and knowledge bases — giving it accurate, up-to-date answers grounded in your actual data instead of general internet knowledge. All running privately on your infrastructure, with your data never leaving your environment.

Private Deployment • HIPAA & CMMC Compliant • No Data Leaves Your Network

95%+
Answer Accuracy
With RAG
10x
Faster Than
Manual Search
100%
On-Premise
Data Processing
23+
Years Cybersecurity
Experience
The Challenge

Why Standard AI Gets Your Data Wrong

Large language models are trained on public internet data — not your internal documents, policies, or procedures. Without access to your specific knowledge, AI gives generic answers that sound confident but miss critical details.

Knowledge Cutoff Problem

AI models are frozen in time. They don’t know about your latest policies, recent regulatory changes, new products, or updated procedures. RAG solves this by feeding current information to the model at query time.

Institutional Knowledge Loss

Critical knowledge lives in scattered documents, tribal knowledge in employees’ heads, and siloed databases across departments. RAG makes all of it instantly searchable and queryable through natural language.

Hours Wasted Searching

Knowledge workers spend 20–30% of their day searching for information across emails, documents, wikis, and databases. RAG reduces this to seconds by providing instant, cited answers from across all your data sources.

The Solution

Enterprise RAG — AI Grounded in Your Data

How RAG Works — The Architecture

RAG combines the reasoning power of a large language model with the accuracy of your specific data. When a user asks a question, the system retrieves the most relevant documents from your knowledge base and provides them to the LLM as context — ensuring answers are grounded in facts, not fabrications.

Vector Database
Your documents are chunked, embedded into vectors, and stored for lightning-fast semantic search
Retrieval Engine
Finds the most relevant document chunks for each query using hybrid semantic + keyword search
Private LLM
Synthesizes retrieved information into accurate, natural language answers with source citations

Key Capabilities

  • Source citations — every answer includes references to the specific documents and pages it drew from, so users can verify accuracy
  • Multi-format ingestion — PDFs, Word documents, spreadsheets, emails, databases, wikis, and web pages are all indexed and searchable
  • Automatic updates — when documents change, the index updates automatically so the AI always has the latest information
  • Access controls — users only get answers from documents they have permission to view, respecting your existing permission structure
Use Cases — Where RAG Delivers the Most Value
Internal Knowledge Base
Ask questions about your policies, procedures, benefits, IT setup guides, and onboarding materials in plain English. New employees become productive in days instead of weeks.
Policy & Compliance Lookup
Instantly find the relevant HIPAA policy, CMMC control, or SOX requirement for any situation. No more digging through 500-page policy documents to find one paragraph.
Research & Analysis
Query across thousands of research papers, reports, case studies, or market analyses. Get synthesized insights with citations instead of reading dozens of documents manually.
Customer Support Knowledge
Connect AI to your product documentation, past tickets, and troubleshooting guides. Support agents get instant, accurate answers for customer questions instead of searching multiple systems.
Legal Document Research
Search across contracts, case files, court opinions, and regulatory filings using natural language. Find relevant precedents and clauses in seconds instead of hours of manual review.
Medical Literature Review
Query medical journals, clinical guidelines, drug databases, and internal protocols. Clinicians get evidence-based answers with source citations for informed decision-making.
Our Deployment Process
Data Source Inventory
We catalog all your knowledge sources — file shares, SharePoint, wikis, databases, email archives, and more. We identify which data sources to include, access controls required, and any compliance considerations.
Ingestion Pipeline Setup
Documents are parsed, chunked into optimal-sized segments, and embedded into vector representations. We configure connectors to your data sources for automatic indexing as documents change.
Retrieval Optimization
We tune chunk sizes, embedding models, retrieval strategies, and re-ranking to maximize answer accuracy for your specific document types and question patterns.
LLM Integration & Testing
The retrieval pipeline is connected to your private LLM with prompt engineering optimized for accurate, cited responses. We test with real questions from your team and iterate until accuracy targets are met.
Production Deployment
The complete RAG system is deployed on your infrastructure with a user-friendly interface, API access, monitoring, and comprehensive logging. All data stays within your security perimeter.
Why Petronella for RAG?

RAG systems handle your most sensitive documents — policies, contracts, patient records, financial data. The security of the RAG pipeline matters as much as the accuracy of the answers.

  • Security-first architecture — document ingestion, vector storage, and LLM inference are all encrypted and access-controlled
  • Compliance expertise — we understand HIPAA minimum necessary requirements, CMMC CUI handling, and attorney-client privilege constraints that affect what data can be indexed
  • Private deployment — your documents never leave your infrastructure. No cloud vector database, no third-party embedding API, no external LLM
  • Enterprise integration — we connect to your existing systems securely, respecting your identity provider, access controls, and audit requirements
FAQ

Frequently Asked Questions

What is RAG and how is it different from fine-tuning?
RAG (Retrieval Augmented Generation) gives the AI access to your documents at query time by retrieving relevant passages and including them in the prompt. Fine-tuning permanently changes the model’s weights. RAG is best for factual Q&A over large document collections that change frequently. Fine-tuning is best for teaching the model new skills, output formats, or domain terminology. Many enterprise deployments use both together.
What types of documents can RAG process?
Nearly any text-based document: PDFs, Word documents, Excel spreadsheets, PowerPoint presentations, emails, web pages, markdown files, CSV data, database records, SharePoint pages, Confluence wikis, and plain text files. We also support OCR for scanned documents and can process structured data from SQL databases.
How accurate are RAG-powered answers?
Well-implemented RAG systems typically achieve 90–95%+ accuracy on factual questions when the answer exists in the document collection. The key factors are chunk size optimization, embedding model quality, retrieval strategy, and prompt engineering — all of which we tune specifically for your data. Every answer includes source citations so users can verify accuracy.
Does RAG work with sensitive or classified data?
Yes. Our RAG deployments run entirely on your infrastructure with no external API calls. The vector database, embedding model, and LLM are all on-premise. For classified or CUI environments, we deploy in air-gapped configurations where no data can leave the network. Access controls ensure users only retrieve documents they are authorized to view.
How long does a RAG deployment take?
A basic RAG system with a single data source can be operational in 2–3 weeks. Enterprise deployments with multiple data sources, access controls, and custom UI typically take 4–6 weeks. The initial document indexing time depends on collection size — thousands of documents can be indexed in hours, millions may take a few days.

Related Services

Ready to Unlock Your Organization’s Knowledge?

Get a free RAG assessment. We’ll evaluate your data sources, identify high-value use cases, and show you how RAG can make your team faster and more accurate — with your data never leaving your control.

No obligation • Private deployment • Cited answers from your data