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On-prem AI infrastructure for European teams

On-prem AI infrastructure. Under your control.

We deploy models and build custom agents on servers you control. Turn documents and company knowledge into practical workflows, with clear access rules.

Book a project call
See our work

Start with one workflow. Agree the scope before we build.

Our team has
worked with.

On-prem AI infrastructure

AI that belongs in your business.

From model deployment to the agents people use every day. We handle the engineering, integrations, and ongoing support.

Keep it in-house.

Models and company knowledge, connected and deployed within the boundaries agreed with IT.

On-prem AI infrastructure

Give work a head start.

Agents that read, prepare, and move work across internal tools. People approve the important steps.

On-prem AI agents & integrations

Make it work, long-term.

Monitoring, model updates, evaluation, and training. Clear responsibilities and a team to call when something needs attention.

On-prem AI operations & support

Work from the people behind Friday

Case studies.

AI product websites designed and built through Proton Lab.

View all work

Explore the engineering behind our team. Agent evaluation, local AI, and developer tools.

RealEvals, Ctrl+Brain & more

Ready to build your on-prem AI infrastructure?

Bring one workflow. We’ll scope the on-prem AI infrastructure and agents it needs.

Book a project call

On-prem AI infrastructure at work

Less searching. More moving forward.

Make documents and company knowledge useful in the moment. Start with a workflow the team does every day.

Your on-prem AI infrastructureWorkflow examples · Not a live demo
IN PRACTICE 01 / 03

From enquiry to a better first draft.

Bring requirements, specifications, and previous proposals into one review-ready brief.

  1. Read the customer’s requirements
  2. Find comparable projects
  3. Flag what your engineer needs to check
The resultA quotation brief, ready for review
On-prem infrastructureYour permissionsHuman approval
A good first project for machine builders

Turn customer requirements, product specifications, and past proposals into a quotation brief. Your engineer reviews the details and makes the decisions.

Explore the workflow

On-prem infrastructure. Your control.

A clear boundary.
At every step.

Keep data local, define access rules, and agree the limits on what an agent can do before connecting it to business systems.

Explore on-prem AI infra

Your on-prem infrastructure

Models, documents, and logs run on company servers, within the boundaries agreed with IT.

Your permissions

Access follows agreed rules. People and agents only use the knowledge they’re allowed to.

Your final say

Review the actions that matter, with a record of what the system retrieved and did.

From pilot to everyday use

One useful project. Then the next.

A small engineering team to scope, build, and operate on-prem AI infrastructure. A clear decision at every step.

  1. 01

    Find the right problem

    Map one workflow, the people doing it, and the data it needs. Agree what improvement would be worth paying for.

    Scope & proposal
  2. 02

    Prove it with your work

    Build a pilot using representative tasks and documents. Test accuracy, time saved, and review effort together.

    A pilot you can evaluate
  3. 03

    Make it part of the day

    Integrate, deploy, and train your team. Put monitoring and support in place before expanding to the next workflow.

    Rollout & ongoing support

The people behind Friday

Small team. Deeply involved.

Work directly with the team building your on-prem AI infrastructure, agents, and integrations.

Backed by Variance

Co-founder · AI engineering & product

Yug Gupta

Previously at AGI Inc · Founder, Proton Lab

Led RealEvals at AGI Inc and founded Proton Lab. His work spans agent evaluation, local AI, and building products from first prototype to launch.

Yug Gupta on LinkedIn Personal portfolio

Co-founder · Product & applied AI

Vedant Nayak

Previously at Dench · Co-creator, Variance

Brings experience from Dench, the AI company building agents for revenue workflows. Co-created Variance with Yug, bringing builders together around ambitious technical work.

Vedant Nayak on LinkedIn About Variance

More about Friday

On-prem AI infrastructure, explained

Good questions.
Straight answers.

Who do you work with?

European SMBs and enterprise teams looking for on-prem AI infrastructure. Machine builders, engineering firms, technical sales, and operations teams are a natural fit. We start with one workflow where better answers or less manual work can make a measurable difference.

Does everything run on our premises?

Models and data run on servers you control. Before building, we agree where documents, logs, and any external integrations run. Hardware requirements and costs are part of the proposal. If private cloud is preferred, we scope that as a separate deployment option.

How do you keep our data secure?

We scope permissions, encryption, logging, retention, and network boundaries with your IT team. Agents get defined permissions and approval steps. Local deployment gives the business control over where AI runs; security still depends on how the system is configured and operated.

What does a first project look like?

A defined on-prem AI pilot, with an agreed scope and price. We assess the available infrastructure, connect representative data, and test one workflow together. You review accuracy, time saved, and review effort before deciding on a wider rollout.

What happens after launch?

We agree an operating plan covering monitoring, model updates, backups, training, and support. The proposal makes hardware, software licences, and ongoing service costs explicit, so responsibilities are clear before rollout.