Vault — Private AI for Internal Knowledge

What is Pragmio Vault?

Private AI on your knowledge—without shipping prompts or documents to the public cloud.

Most companies already have the answers they need.

They’re just buried across documents, tools, and people.

Pragmio Vault is a private AI system built for organizations that want to use AI on that knowledge—without exposing sensitive data to third-party platforms.

Your team can ask questions, search across documents, and get grounded answers from your own files and systems. It runs locally or inside your private infrastructure—not as a generic public chatbot.

What problems does it solve?

When knowledge is scattered, the cost shows up in payroll, rework, and decisions made without full context.

This isn’t “we could be more organized.” It’s hours burned every week, answers that change depending on who you ask, and risk that grows when the one person who knows leaves.

What that costs you today

Teams waste hours looking for answers that already exist.
Decisions are made without full context.
The same questions are answered repeatedly.
Critical knowledge disappears when people leave.

What this changes

Not just a better system—a different day-to-day for how work gets done.

Before Answers depend on who you ask.

After Answers are instantly accessible—within the permissions you already use.
Before Documents sit in folders nobody wants to open.

After Internal files become something you can query and summarize in one place.
Teams stop asking the same questions over and over.
New employees ramp faster because answers live where work happens.
Decisions use actual data and internal context—not guesswork.
Internal knowledge stops walking out the door when people leave or change roles.

Key features

Business-readable capabilities—built for private knowledge work.

1 · Chat

Private AI Chat

Ask questions in natural language and get answers based on your internal data.

2 · Search

Document Intelligence

Search across large volumes of files and retrieve relevant information quickly.

3 · Grounding

Grounded Answers

Responses can include source references and supporting document context.

4 · Access

Access Control

The system can be designed to respect user roles and document permissions.

5 · Deploy

Local Deployment

Runs inside your infrastructure for privacy, control, and predictable ownership.

6 · Data

Custom Data Ingestion

Supports ingestion from files, exports, websites, internal documentation, and more.

How Pragmio Vault Works

Three layers executives can remember; technical depth lives under the hood when you need it.

You don’t need to understand this to use it—but here’s how it works under the hood.

AI Layer

The interface your team uses, plus the models that reason on your hardware—not a vendor’s cloud.

Employees work in a secure web experience to ask questions and review answers. Behind that, the model runs inside your environment (for example Llama, Mistral, Qwen, or DeepSeek-style local deployments). Model choice follows use case, privacy needs, user load, and available GPUs.

Data Layer

Operational records and “meaning” for search—kept separate on purpose.

A main application database holds users, roles, chat history, audit logs, and settings (often PostgreSQL or MySQL). A vector store holds embeddings so retrieval works by meaning, not only keywords (e.g. pgvector, Qdrant, Weaviate). Answers are grounded with RAG: retrieve relevant chunks, add them to the model context, then generate.

PostgreSQL + pgvector is a strong default for many private deployments.

Security Layer

Who can sign in, what they can see, and what gets logged.

Designed around your infrastructure: HTTPS, authentication (email, SSO, Microsoft, Google, or internal IdP), role-based access, document-level permissions, audit logging, and permission-aware retrieval so search respects the same rules as your files. The full security picture is spelled out in Security and Privacy below.

Under the hood

Ingestion, indexing, permissions, deployment, stack—same substance, optional depth.

Ingestion & pipeline

The ingestion pipeline turns company files and internal content into searchable, AI-ready knowledge.

A typical ingestion flow includes:

file upload or source connection
text extraction
content cleaning
chunking into smaller sections
embedding generation
storage of chunks and metadata
indexing for retrieval in chat

Supported sources may include: PDF, DOCX, TXT, CSV, website pages, internal wiki pages, email exports, database exports, CRM or ERP exports.

Important note on images: Images, scanned files, diagrams, and visual documents require additional handling. Support depends on document quality, layout complexity, and whether OCR or multimodal processing is part of the deployment scope.

Indexing & retrieval (RAG)

Pragmio Vault uses retrieval-augmented generation (RAG) to produce answers grounded in your own data.

Typical flow: the user asks a question → the system searches for relevant content → the most useful results are added to the model context → the model generates an answer based on that material.

To improve quality, the retrieval layer can include:

hybrid search, combining semantic and keyword search
reranking, to prioritize the most relevant chunks
source references in answers
permission-aware retrieval, so users only access content they are allowed to see

Chat app & API stack

What teams see. A web-based interface to ask questions, review answers, and work with internal knowledge.

What we typically build. A frontend for users, a backend API for requests, storage for sessions and conversation history, and an authentication layer for secure access.

Typical stack. Frontend: React / Next.js. Backend: Node.js or Python FastAPI. Authentication: email login, SSO, Microsoft, Google, or internal auth.

For organizations with stricter security requirements, desktop-based or internally restricted deployments can also be considered.

Models & deployment choices

The model is the reasoning layer; in private deployments it runs on your infrastructure rather than through external AI providers.

Examples of model families often used in private setups: Llama, Mistral, Qwen, DeepSeek local variants.

Important: Model selection depends on the use case, privacy requirements, expected number of users, and available hardware. Hardware sizing and options such as NVIDIA DGX Spark are covered in Deployment and Hardware.

Security and Privacy

Designed for organizations that need stronger control over where data lives and how it is accessed.

Your data never leaves your control.

Security design can include:

deployment inside your infrastructure
HTTPS and internal network protections
encrypted storage for sensitive data
user authentication and role-based access
document-level permission controls
audit logging
backup strategy and recovery planning
administrative controls for reviewing uploads and access

The exact security model depends on your infrastructure, compliance requirements, and internal access policies.

Deployment and Hardware

Private deployment means hardware matters—NVIDIA DGX Spark is one option, not the product identity.

Your data never leaves your control.

Pragmio Vault is designed for private deployment, which means hardware matters. The right configuration depends on the size of the knowledge base, the chosen model, the number of concurrent users, and the expected response speed.

For smaller or focused deployments, dedicated local AI systems such as NVIDIA DGX Spark can be a strong fit for secure private AI chat, document retrieval, and internal assistant workflows. Spark is one deployment option—not the product itself.

Based on current practical expectations, a single DGX Spark can be a strong option for:

Llama-family models up to around 70B
Qwen-family models around 32B to 35B very comfortably
local agent-style workflows
private RAG systems
multimodal-capable stacks in selected configurations

More demanding workloads depend on model size, quantization, context window, and concurrent usage. Heavier 70B to 120B deployments, larger MoE models, very large context windows, or multi-agent stacks may require more careful sizing or larger infrastructure.

Buyer note: Large data volume and large user concurrency are different infrastructure problems. A system may index a very large knowledge base while still requiring additional hardware to support many active users at once.

Who it’s for

If any of these sound familiar, you’re not shopping for a generic chatbot—you’re fixing how knowledge actually moves inside the business.

Companies where people say “I think it’s somewhere…”
Teams where answers depend on specific individuals
Organizations that cannot risk using public AI on sensitive work
Businesses losing time to internal knowledge chaos

Private AI on your own data—no prompts or documents shipped to the public cloud.

Interested in a private AI system for your organization?

We can help design a deployment that matches your infrastructure, privacy requirements, and data environment.

Get a Vault setup plan

Private AI for your company’s internal knowledge