Private AI: The Complete Guide for Enterprises
Private AI means running AI models, agents and retrieval entirely inside an environment your enterprise controls — your cloud account, your data centre, or a boundary we operate under contract — instead of sending prompts and data to a public API. This page is the starting point for everything we've written about it.
Four pages make up the cluster below. Start wherever matches what you need: the deployment page if you're evaluating commercial viability, the agents page if you want AI that takes action rather than just answers, the architecture guide if your security team needs the actual technical diagram, or the case studies if you just want proof it's already running.
What Is Private AI?
Private AI is the practice of running language models, retrieval and AI agents inside a boundary the enterprise itself controls — a customer VPC, on-premise hardware, a dedicated private cloud, or fully air-gapped — rather than calling a shared, multi-tenant API run by a model vendor. Nothing about a prompt, a document or a customer record leaves that boundary unless it's explicitly routed out, and every request is logged where the enterprise's own team can read it. It isn't one product — it's an architectural commitment that applies to everything we build: private LLMs, private RAG, and private AI agents that call real tools.
Four Pages, One Topic
Each page below stands on its own — read one, or read all four in order.
Private AI Deployment
Deployment options, the 13-step delivery process, and when private AI is worth it over a public API.
Private AI Agents
How private agents retrieve knowledge, call real tools, respect permissions, and get human approval before acting.
Private AI Architecture
The actual reference architecture — every component, all four deployment boundaries, one diagram.
Private AI Case Studies
Four real deployments in production, one per processing path in the architecture.
Private AI, in Plain Language
The vocabulary that shows up across all four pages, defined once, here.
Letting a model answer using documents retrieved from your own knowledge base, instead of only what it learned during training.
The AI model itself — the component that reads a prompt and generates a response.
Single sign-on / identity and access management — how a request proves who's making it before anything else happens.
Permissions tied to a role rather than an individual — the same model an AI agent's access should follow.
An isolated network boundary inside a cloud provider (AWS, Azure, GCP) that only your organisation can reach.
Running the entire stack on hardware inside your own data centre, rather than in any cloud.
A deployment with no route to the public internet at all — the most restrictive isolation level.
Rules that check a model's output, or a proposed action, against policy before it reaches a user or a live system.
The index a retrieval system searches to find relevant documents — stores content as embeddings rather than plain text.
Further training a model on an organisation's own data so it performs better on that organisation's specific tasks.
Before You Read Further
No — on-premise is one of four deployment options under the private AI umbrella, alongside customer VPC, private cloud and air-gapped. Private AI is the broader architectural commitment; on-premise is one way to physically realise it.
Not necessarily. Private AI earns its cost when the data is sensitive, a customer contract restricts where data can go, or a security review has already blocked a public-API build. If none of that applies, a public API may genuinely be the faster, cheaper choice — we'll say so.
The economics are different, not automatically worse — infrastructure you size and own instead of a per-token rate that can change. The deployment page has the fuller comparison.
Yes — that's the default pattern, not an add-on. The agents page covers how permissions and human approval are handled around those tool calls.
Bring your security and infrastructure leads to a discovery call — the deployment question is the one we want to start with, not the last thing we figure out.
Start wherever makes sense — we'll meet you there.
Whether you're evaluating feasibility, scoping an agent, reviewing the architecture, or just want proof this works — one conversation covers all four.