Miigwech AI Solutions designs private retrieval-augmented generation (RAG) systems that let organizations search, summarize, and ask questions across internal documents without sending their knowledge base to public cloud AI services.

Run the AI, document retrieval, and knowledge base on infrastructure you control— including your own workstation, GPU server, or approved on-premise environment.

What is a private RAG system?

RAG stands for retrieval-augmented generation. Instead of relying only on a language model's general training, a RAG system retrieves relevant passages from your approved documents before producing an answer. This can make an AI assistant more useful for organization-specific policies, contracts, research, procedures, and knowledge bases.

A private RAG system keeps that workflow within your chosen environment. Your source documents, embeddings, prompts, retrieved passages, and model inference can remain on systems you administer rather than being sent to a public AI service.

Use AI with documents you need to protect

Teams often want faster ways to navigate large document collections, but cannot accept uncontrolled external sharing of sensitive information. A private RAG deployment can support secure internal research and document workflows while maintaining direct control over where data is stored and processed.

• Policies, procedures, and internal handbooks
• Contracts, legal research, and case-related material
• Technical documentation and engineering knowledge bases
• Reports, meeting records, and operational documents
• Approved research collections and internal reference material

The right document sources and access rules depend on your environment. A private RAG system should be designed around the information your team is authorized to use.

How local RAG works

1. Choose the document collections and users that the system is allowed to access.
2. Extract and index useful text from the approved documents in a local knowledge base.
3. Retrieve relevant passages when a user asks a question.
4. Send the question and retrieved context to a local AI model running on hardware you control.
5. Return an answer with source references so users can review the underlying material.

The architecture can be adapted to available hardware, performance requirements, document formats, and the security controls your organization needs.

Keep AI inference and knowledge under your control

miigEngine is a privacy-first AI inference approach designed to run on your own computer and GPU. A private RAG system can pair local model inference with locally managed document retrieval so that sensitive prompts and knowledge-base content stay within your environment.

Local deployment does not automatically solve every security or governance requirement. Miigwech AI Solutions works with organizations to define practical boundaries around access, document intake, retention, auditing, and operational ownership before deployment.

Who private RAG is for

Private RAG is a fit for organizations that need useful AI assistance but want more control over sensitive information than a public, consumer AI workflow can provide. This may include legal teams, professional-services organizations, technical teams, public-sector groups, research organizations, and organizations with data-sovereignty or confidentiality requirements.

Australian Legal RAG (OALC)

Miigwech AI Solutions can deploy miigEngine with the Open Australian Legal Corpus (OALC) RAG and database in place of the standard A2AJ legal RAG module. This provides a private, locally deployed AI research environment for searching, summarizing, and exploring Australian legal material.

Custom Legal RAGs

miigEngine uses a modular RAG architecture. The knowledge base and retrieval module can be adapted for a specified country, province, state, territory, nation, or regulatory area. Miigwech AI Solutions can deploy an existing legal RAG where available, or create and curate a custom RAG using approved legal sources and your organization’s authorized internal documents.

Private RAG deployment process

1. Discovery: Define the user workflow, document sources, access boundaries, and success criteria.
2. Prototype: Validate retrieval quality, local model performance, and source citations against approved sample material.
3. Deployment: Configure the system on the selected local or on-premise infrastructure.
4. Refinement: Improve document preparation, prompts, retrieval, and user experience using real feedback.

Frequently asked questions

Does private RAG mean nothing ever leaves our network?

It can be designed that way, but the answer depends on the implementation. The deployment should explicitly define which services, telemetry, model downloads, updates, and integrations are permitted. Miigwech AI Solutions can help design a local-first architecture around those requirements.

Can a private RAG system cite the documents it used?

Yes. A well-designed workflow can return source excerpts, document names, page references, or links to the retrieved material so users can verify an answer rather than treating the AI response as an authority.

Can it work with legal documents?

A private RAG workflow can support internal search, summarization, and question-answering across approved legal documents. It should be used with appropriate professional review and should not be represented as a substitute for legal advice or human judgment.

What hardware is required?

Requirements vary with the model size, number of users, response-time expectations, document volume, and deployment model. A discovery and prototype phase is the right time to assess whether an existing workstation, dedicated GPU server, or another environment is appropriate.

Discuss a private RAG system

If your organization needs to work with sensitive documents while retaining control over AI processing and knowledge-base data, Miigwech AI Solutions can help evaluate a local-first RAG approach.

Contact Miigwech AI Solutions