AI Automation

Private RAG Systems

Enterprise retrieval-augmented generation that keeps your data within your compliance boundary. Query your documents with AI — without exposing them to third-party models or cloud providers.

Retrieval-augmented generation (RAG) allows your teams to ask natural-language questions and receive accurate, cited answers drawn from your own documents and data. But for trust-critical industries, the standard approach of sending documents to third-party AI providers creates unacceptable compliance and security risks.

TrustEdge Private RAG solves this by deploying the entire RAG pipeline — document ingestion, embedding generation, vector storage, and AI inference — within your compliance boundary. Whether that means your own data center, a private cloud, or an Azure AI secure enclave, your sensitive data stays under your control at every stage.

The result is an AI-powered knowledge system that your compliance team, your security team, and your users can all trust.

What's Included

Every Private RAG engagement is scoped to your specific requirements. Here are the core capabilities we deliver.

On-Premise or Private Cloud Deployment

Your RAG system runs entirely within your infrastructure. Documents never leave your environment, and embeddings are generated and stored locally or within your private cloud.

Multi-Source Document Ingestion

Ingest and index documents from internal file shares, document management systems, databases, and email archives. Support for PDF, DOCX, XLSX, and structured data formats.

Role-Based Access Controls

Document-level and collection-level permissions ensure users only query content they are authorized to access. Integrates with Active Directory, SAML, and OIDC providers.

Citation and Source Tracking

Every AI-generated answer includes verifiable citations back to the source documents, paragraph, and page number. Full auditability for compliance reviews.

Hallucination Guard Rails

Built-in confidence scoring, retrieval quality checks, and answer grounding verification reduce hallucinations. When the system is not confident, it says so.

Compliance-Ready Architecture

Designed for HIPAA, SOC 2, and PCI-DSS environments. Encryption at rest and in transit, comprehensive audit logging, and data retention policy enforcement.

Architecture Overview

A Private RAG system is a multi-layer pipeline, each component deployed within your security perimeter.

User Interface

Chat interface, API endpoints, or embedded widgets

Query Engine

Natural language processing, intent classification, query routing

Retrieval Layer

Vector search, hybrid search, re-ranking, and filtering

Embedding Store

Private vector database (Pinecone, pgvector, or Weaviate)

Document Pipeline

Ingestion, chunking, OCR, and metadata extraction

Source Systems

File shares, DMS, databases, email — your existing data sources

Use Cases by Industry

Healthcare

Clinical protocol lookup and summarization
Medical records search with HIPAA-compliant access controls
Drug interaction and formulary query systems
Patient education content generation from approved sources

Legal

Case law research within privileged document repositories
Contract clause search and comparison
Regulatory compliance document retrieval
Matter-specific knowledge bases with access restrictions

Financial Services

Regulatory filing research and cross-referencing
Investment research across proprietary datasets
Compliance policy lookup for relationship managers
Internal audit evidence retrieval and summarization

How We Work

Discovery

We map your document sources, user roles, compliance requirements, and integration needs.

Architecture

We design the RAG pipeline, embedding strategy, and security architecture for your environment.

Implementation

We deploy the system, ingest your documents, tune retrieval quality, and integrate with your workflows.

Optimization

Ongoing monitoring, retrieval quality improvement, and expansion to additional document collections.

Results

Document search time reduction

Third-party data exposure

Citation accuracy

Typical deployment timeline

Frequently Asked Questions

Does our data leave our network?

No. Private RAG systems are deployed entirely within your infrastructure or private cloud. Documents, embeddings, and queries never leave your compliance boundary.

What document types are supported?

We support PDF, DOCX, XLSX, TXT, HTML, and structured data from databases and APIs. OCR processing handles scanned documents and images containing text.

How do you handle document access permissions?

Our RAG systems integrate with your existing identity provider (Active Directory, SAML, OIDC) to enforce document-level access controls. Users only see results from documents they are authorized to access.

What about hallucinations?

We implement multiple safeguards: confidence scoring, retrieval quality checks, source grounding verification, and citation requirements. When the system cannot answer confidently from your documents, it indicates the limitation rather than generating unsupported content.

Can we use our own AI models?

Yes. Private RAG can work with open-source models hosted on your infrastructure, Azure OpenAI Service within your tenant, or AWS Bedrock within your account. We help you choose the right model for your use case and compliance requirements.

How long does implementation take?

A typical Private RAG deployment takes 6-12 weeks from discovery through production, depending on the number of document sources, complexity of access controls, and integration requirements.

Technical AI Bootcamps AI Academy Model Deployment and Orchestration AI Operations

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Interested in this AI Automation solution?

Let's discuss how it fits your compliance and operational requirements.

Schedule a Consultation (415) 644-8208