Private AI infrastructure for data and AI leads

Open-weight models, retrieval over your internal data and MCP connections to your apps, running on GPUs on your premises or in the region you choose. Operated for you, so your team builds instead of patching.

Start with one workloadBook a 30-minute briefing

Models
Open-weight, chosen per use case
GPUs
On your premises or dedicated in Switzerland
Retrieval
Indexed inside your perimeter, with citations
Agents
MCP servers over OAuth, every call recorded

An AI stack your security team can sign off

The hard part of AI in a company is rarely the model. It is the data it may read, where it runs and who keeps it running. Pilae takes the operations so your team can work on the use cases.

Private models

Open-weight models such as Llama, Mistral and Qwen, served by vLLM or Ollama on your own GPU servers or on dedicated GPU machines in the Pilae Cloud region you choose.

RAG on internal data

Documents, wikis, file shares and databases indexed where they live. Answers cite the passages they draw on.

Permissions from your directory

Retrieval keeps the permissions of each source and identifies users through your identity provider, so a user only gets answers from documents they may already read.

MCP for your agents

Model Context Protocol servers next to your apps, so Claude, Cursor or your own agents act on real systems as the signed-in user.

Assistants and pipelines

Open WebUI or LibreChat for chat, Dify for assistants, n8n and Windmill for pipelines that move and prepare data, and Langfuse to trace and score what the models answer.

Updates you approve

The Pilae Agent plans each model, runtime and app update, takes a verified backup and applies it in your window after your approval.

Your team should build use cases, not run GPU drivers

A private AI platform is a model server such as vLLM or Ollama, a vector index such as Qdrant, a chat layer, identity, pipelines, tracing and GPUs, each with its own release cycle. Someone has to patch them, back them up and answer the alerts. With Pilae, that is us: monitoring around the clock on every plan, and around-the-clock incident response on Enterprise. Your data and AI team keeps ownership of the models, the prompts and the use cases.

The private AI offer covers the organisation-wide assistant. This page is about the platform underneath it, and what you can build on top.

Models where your data is allowed to be

We run open-weight models on the hardware you choose: your own GPU servers on premises, dedicated GPU machines in Pilae Cloud in the region you choose, or both in a hybrid setup. For sites with no outbound connection, the whole stack runs air-gapped on Enterprise plans. The contract names the location, and data residency is not a setting a vendor can change.

Retrieval on internal data

Retrieval-augmented generation grounds answers in your own sources: Nextcloud, Outline, file shares and databases such as PostgreSQL. The index lives next to the data. Permissions come from your directory through Keycloak or your existing identity provider, so retrieval never widens anyone’s access.

Agents that reach real systems, on record

MCP servers give Claude, Cursor or your own agents a standard way to read and act on your apps. Model Context Protocol is an open standard, so the same servers work with any client that supports it. Each person signs in over OAuth, the agent acts with their permissions, and every tool call lands in the audit trail your security team can export to its SIEM. Pipelines in n8n and Windmill prepare the data and run the agents on a schedule, and Langfuse records each model call with its prompt, answer, cost and latency, so a new model or prompt can be compared with the one it replaces. Before an agent touches production, it can run in a sandbox: an isolated copy of the app and its data, inside your perimeter.

Operated under contract

The Pilae Agent plans every update and waits for your approval. Monitoring probes every service every 60 seconds, and alerts reach Pilae engineers around the clock. Backups are daily, encrypted, offsite in the country you choose, and restore-tested every month. To scope a pilot, tell us about your project.

From first use case to production

  1. Scope

    We agree the use cases, the data sources, the users and where the GPUs run: your premises, the Pilae Cloud region you choose, or both.

  2. Size

    We match models to the hardware and the workload, and write down which models run in which environment.

  3. Build

    We deploy the model server, the retrieval index, the chat or assistant layer and the MCP servers on your private network.

  4. Connect

    Single sign-on, the first data collections and the first agent connections, with permissions set per group.

  5. Run

    Monitoring every 60 seconds, alerts to Pilae engineers around the clock, and every change planned and approved.

What your contract includes

Data location
Named in the contract. Prompts, embeddings and documents do not leave it.
Model list
The models in each environment are listed. Adding or changing one is a planned, approved change.
External APIs
Off by default. Routing selected users to an external model is your decision, under your own contract.
Audit trail
Changes, sign-ins and MCP tool calls on record, exportable to your SIEM.
Logs
Kept on the machines that produced them. The console reads them on demand.
Backups
Daily encrypted offsite backups in the country you choose, with a monthly restore test recorded in the console.
Availability
A monthly availability commitment with service credits: 99.5% on Business, 99.9% on Enterprise.

The apps behind it

vLLM

An OpenAI-compatible inference server for open-weight models, run on GPUs in Switzerland, the EU or your own datacentre, so prompts and answers never reach a model vendor.

Replaces OpenAI API, Azure OpenAI

Ollama

A server that loads open-weight models on demand behind an OpenAI-compatible API, run on GPUs in Switzerland, the EU or your own datacentre so prompts never reach a model vendor.

Replaces OpenAI API, Azure OpenAI

Qdrant

A vector database for semantic search and RAG, run on your own hardware or in Switzerland and the EU, so the embeddings of your documents stay where the documents are.

Replaces Pinecone, Weaviate Cloud

Langfuse

Tracing, evaluation and prompt management for LLM applications, run in Switzerland, the EU or your own datacentre so the prompts and answers it records stay with you.

Replaces LangSmith, Helicone

Open WebUI

A chat interface over models you host, so prompts and the documents people paste into them never leave your network.

Replaces ChatGPT Team, Microsoft Copilot

Dify

A builder for AI apps, agents and retrieval workflows that runs next to your documents and your models instead of at a platform vendor.

Replaces OpenAI Assistants, Azure AI Studio

n8n

Workflow automation your own team writes, running next to the systems it talks to instead of reaching them across the internet.

Replaces Zapier, Make

Windmill

Scripts, workflows and internal tools in Python, TypeScript, Go, Bash or SQL, run by workers on your own machines and operated by Pilae.

Replaces Retool Workflows

PostgreSQL

Managed PostgreSQL on dedicated machines in Switzerland, the EU or your own datacentre, with backups you can restore to the minute and upgrades you approve.

Replaces Amazon RDS, Azure Database for PostgreSQL

Questions

Can we run large language models without sending data to a US provider?

Yes. The models run on GPUs on your premises or on dedicated machines in the Pilae Cloud region you choose. Prompts, documents and embeddings stay in that environment, and no external model is called unless you decide it should be.

Do we need to buy GPUs?

No. You can start on dedicated GPU machines in the Pilae Cloud region you choose and move the workload to your own servers later, or run both. We operate either.

How does retrieval handle access rights?

Documents are indexed inside your perimeter with the permissions of their source. At query time the user is identified through your SSO, and only passages they are allowed to read are given to the model.

What is MCP and why does it matter for us?

Model Context Protocol is an open standard that lets AI assistants call tools in other systems. We run MCP servers next to your apps, behind OAuth sign-in, so an agent acts with the user's own permissions and every call is logged.

Can the stack run air-gapped?

Yes. On Enterprise plans the models, the retrieval index and the apps run with no outbound connection. Model and software updates arrive as planned changes that you approve.

Related

Bring one use case and one data source.

Thirty minutes with an engineer: we look at your data, your hardware options and your first use case, and scope a pilot with a fixed price.