Line drawing of a sealed nose fairing with a long propellant tank enclosed inside it.

A private AI workspace your company owns

Right now your team is on a dozen separate AI subscriptions, all used differently, with no visibility into where company data is going. Phos AI Labs deploys one private workspace with your controls, your context, and usage you can see.

Book a strategy call →Take the AI Readiness Scorecard

A private AI workspace is a single company-owned AI environment where the whole team works, running inside your own infrastructure with your access controls, your data, and your company's context loaded by default. It replaces a dozen individual subscriptions with one managed environment your security team can approve. One workspace turns scattered spend into a baseline you can manage, with a report showing who is using what and whether anything came back.

OpenAI Select Partner and Claude Partner Network

Claude (Anthropic) Partner · Select OpenAI Partner · CCA-F certified team

  • Cleared institutional procurement

    legal and security review passed before launch

  • 4 weeks

    to a deployed workspace, then it runs

Trusted across 450+ builds

  • American Express
  • Coca-Cola
  • Sotheby's
  • Medtronic
  • Dataiku
  • Margaritaville
  • Zapier
  • Whitecoat Planning

Why does buying everyone a license never turn into AI adoption?

Because a license is a door, and nobody agreed on what happens after people walk through it. Twelve subscriptions produce twelve private habits, and none of them are visible from the outside.

  • Twelve subscriptions, twelve standards

    Everyone is using a different tool a different way. Same task, different quality, and no version anyone else can copy.

  • Data going somewhere nobody approved

    Some of it is going into free-tier tools with client names still attached. Your security team cannot sign off on something they cannot see.

  • No way to measure the investment

    Leadership is paying for a dozen seats and has no report showing who uses them, on what, or whether anything came back.

  • Context re-explained every session

    Every person types the company's background into the model from scratch, because there is no shared place for it to live.

The UK Department for Business and Trade gave a thousand people Microsoft 365 Copilot licenses for three months and published what happened. Average use came to 1.14 actions per person per working day. Seventy-two percent said they were satisfied. The evaluation found no robust evidence that the time savings turned into productivity.

That is what a seat count buys on its own. Access changes who can open the tool. It does not change where the company's context lives, what good output looks like, or whether anyone can see the return.

One environment, your infrastructure, your rules

This engagement consolidates and deploys.

Your team keeps the capabilities they already know, and the workspace adds the shared context, the controls, and the visibility that only comes from one managed environment.

What you walk away with

  1. 01

    Private workspace

    A single company-owned AI environment with your branding, running inside your infrastructure.

  2. 02

    Access controls by role

    Who can see what, enforced at the data layer rather than hidden in the interface.

  3. 03

    Shared knowledge base

    Your company brain, documents, and history loaded once and available to everyone, so nobody re-explains the company.

  4. 04

    Shared skills and projects

    The prompts and workflows that work, saved as company assets instead of living in one person's history.

  5. 05

    Stack connections

    Wired into the tools your team already runs, so the workspace sits where the work happens.

  6. 06

    Usage dashboards

    Adoption by department: who is using AI, on what, how often, and where the gaps are.

  7. 07

    Consolidated billing

    One invoice, one infrastructure, and the scattered subscriptions retired.

  8. 08

    Security sign-off pack

    The data-handling documentation your security and legal teams need, delivered before anyone starts using it.

  9. 09

    Migration plan

    How each team moves over, in what order, with the existing licenses wound down cleanly.

  10. 10

    Model choice per task

    Claude, GPT, and others available in one place, selected by accuracy and cost per job.

What actually happens once you start?

Audit, deploy, migrate, then measure. Four weeks to a working workspace, and it keeps running after that.

  1. 01

    We audit what your team already uses

    Who has subscriptions, on what tier, and where company data has been going. The full picture gets mapped before anything gets built.

  2. 02

    We deploy your private workspace

    A single company-owned environment with your branding, your access controls, and your data inside your own infrastructure, holding the same capabilities your team already knows.

  3. 03

    Your security team signs off

    The data-handling documentation goes to security and legal before a single person is invited in. Approval comes first, rollout comes after.

  4. 04

    We migrate the team and retire the subscriptions

    Everyone moves over department by department, usage dashboards go live, and the scattered licenses get wound down instead of quietly renewing.

  5. 05

    We monitor and close the gaps

    The workspace is live and we stay on it: tracking adoption, surfacing usage patterns, and finding the gaps your team has not noticed yet.

Where does this sit in the engagement?

  1. 01

    AI Consulting

    The audit that finds where AI belongs and what it is worth.

    See AI Consulting →
  2. 02

    AI Enablement

    Your team gets fluent on the work AI is about to touch.

    See AI Enablement →
  3. 03

    AI Implementation

    The workspace is where your team works inside this phase.

    See AI Implementation →

This is for you if:

  • Six to twelve people have their own AI subscriptions and nobody is managing them.
  • Your security team has concerns about where company data is going.
  • You want visibility into who is using AI and whether it is working.

This is not for you if:

  • One or two people are experimenting and the rest have not started.
  • You want a customer-facing AI product, which is a different engagement.
  • Nobody has mapped how the company works yet, in which case start with the company brain.

What the workspace sits on and connects to

The workspace is the room your team works in. Two other pieces decide how much it knows and how far it reaches.

  • Company Brain

    The living map of how your company works, loaded into the workspace so every answer starts with your context instead of a blank page.

  • AI Operating System

    The connective layer between your CRM, inbox, project manager, and docs, so the workspace reaches into the tools where the work actually happens.

Why does a private workspace matter more in some industries?

A law firm, a healthcare practice, and a manufacturer all handle information they cannot paste into a consumer tool, and each of them answers to a different reviewer. The setup follows the rules you actually operate under.

Aviation Manufacturing Healthcare
Finance Advisory Insurance Real Estate
Ecommerce Logistics Staffing
HR SaaS Construction
Field Service Legal Retail
Accounting Marketing Property Management

Don’t see your industry?

If your work runs on drafting, summarizing, or answering from your own documents, there is a fit.

Book a call

What does this actually change?

Companies come to Phos AI Labs paying for a dozen seats they cannot see. They leave with one environment their security team approved, one place the whole team works, and a baseline they can manage.

  1. 01

    A data posture your security team signs

    Everything runs in your infrastructure under your controls, documented well enough to clear review before anyone logs in.

  2. 02

    One place the whole team works

    Same context, same shared skills, same standards. The quality of an answer stops depending on which person happened to ask.

  3. 03

    A baseline you can manage

    Adoption is visible by department, so you know where AI is paying off and where the next investment should go.

Does a private environment actually unlock anything?

It shows up in three places: reviews that get cleared instead of stalled, answers that improve because the context is shared, and hours that come back once the whole team works in one place.

Career Haven

CONTEXT

An institutional grant-writing platform whose customers could not upload proprietary program documents to a consumer AI tool. University procurement flagged it before anyone got to test the product.

WHAT PHOS AI LABS DID

Built role-based isolation at the database query level and an architecture designed to pass review before the first client onboarded. Institutional legal and procurement review cleared, which was the market entry, and token usage fell 40% after the rebuild.

The goal is to show that we are drawing knowledge from experts all the time. It's not just what the AI is making up.

— Ogo Ekwueme, Founder, Career Haven

Read the full story →

Why Phos AI Labs over a team plan or building it in-house?

Phos AI Labs gives your company its own environment, built around how your business works and documented well enough to clear the review that usually stops these projects. That is what separates a deployment from a subscription upgrade.

  1. 01

    We build for the reviewer as well as the user

    The architecture is designed to pass security, legal, and procurement before the first person logs in. We have cleared institutional review, which is the hardest version of that test.

  2. 02

    Your context comes with it

    The workspace ships loaded with your company brain, so answers start from how your business actually operates. The context is what turns a generic tool into something your team trusts with real work.

  3. 03

    The hire you can't make

    A senior AI lead costs $250,000+ and takes six months to find, and standing this up needs infrastructure, security, and adoption work at once. Phos AI Labs brings the whole team the week you start.

How much does a private AI workspace cost?

Setup starts at $10,000, which covers building and deploying the workspace, and there is an ongoing monthly fee that varies with team size and the complexity of the setup. You get both numbers once we know what we are working with.

  • Workspace setup

    from $10,000 fixed

    Audit of what your team runs today, deployment inside your infrastructure, access controls, security sign-off pack, and migration.

    Book a strategy call

  • Ongoing workspace

    Scoped monthly

    Priced by team size and complexity. The workspace running with dashboards, model access, support, and the gaps closed as adoption data comes in.

    Get both numbers

  • Embedded AI department

    up to $50,000 /mo.

    Your full AI team on retainer, with the workspace as the environment where the agents and systems they build actually live.

    Talk through scope

You are already paying for a version of this. A dozen individual subscriptions, a handful of them barely used, plus the hours lost to everyone working a different way. Consolidating that spend into one environment usually covers a meaningful share of the setup, and the license count stops being the thing you are managing.

Keep going

The product this runs on, and the fastest way to see where your team stands.

  1. 01

    Nexus

    The private, company-owned AI workspace Phos AI Labs deploys, seen from the product side.

    Explore →
  2. 02

    AI Readiness Scorecard

    Ten questions, a personalized score. See where your company stands before you talk to anyone.

    Explore →
  3. 03

    Deploy Claude Blueprint

    The six-step guide to rolling Claude out across a team, free.

    Explore →
  4. 04

    Case Studies

    The full library of what Phos AI Labs has shipped, with the numbers behind each build.

    Explore →

In partnership with

  • Anthropic
  • OpenAI
  • Zo
  • Make

FAQs

What is a private AI workspace?
A private AI workspace is a single company-owned AI environment where the whole team works, running inside your own infrastructure with your access controls, your data, and your company's context loaded by default. It replaces a pile of individual subscriptions with one managed environment.
Where does our company data go?
It stays inside your business. The workspace runs in your own environment with your access controls and your security policies, and nothing flows through a shared public system. Your security team reviews the data-handling documentation and signs off before anyone starts using it.
How is this different from buying a team plan?
A private workspace gives your company its own environment with role-based controls enforced at the data layer, your knowledge base loaded in, and usage visibility built around how your business works. A team plan gives everyone access to one vendor's shared environment with none of that in place.
We already pay for ChatGPT, Claude, or Copilot licenses. Is that wasted?
No. The workspace consolidates them. Your team keeps the capabilities they already know, in one place with your branding, your controls, and your data inside your environment, and the scattered subscriptions get retired instead of quietly renewing.
How much does a private AI workspace cost?
Setup starts at $10,000 and covers deployment, access controls, the security sign-off pack, and migration. After that there is a monthly fee scaled to team size and setup complexity. A full embedded AI department, with the workspace included, runs up to $50,000 per month.
How long does deployment take?
About four weeks from kickoff to a workspace your team is working in. That covers auditing what everyone uses today, deploying inside your infrastructure, getting security sign-off, and migrating department by department.
What does month two look like?
You start seeing usage patterns you never had visibility into: which teams lean on AI, which have gone quiet, and where the real opportunities are. That data drives where the next investment goes, and it is the baseline every system built afterward gets measured against.
Other government studies say Copilot saved people 26 minutes a day. Which one is right?
Both, and the difference is the method. The 26 minutes comes from the cross-government trial of roughly 20,000 licences and is a self-reported estimate, and the Department for Work and Pensions trial reported a similar self-reported gain using a comparison group rather than random allocation. The Department for Business and Trade evaluation measured actual usage instead and found 1.14 Copilot actions per person per working day, with no robust evidence that time savings became productivity. A workspace with usage data attached is how you stop guessing either way.

The fastest way to know whether we're the right fit, is a conversation.

STEP 1/2 · ABOUT YOU