Foundry

Offers

Advice. Managed setup. On-prem assurance.

Start with a practical fit review, move to a managed private AI node, or choose a deeper on-prem support model for compliance-sensitive work.

Book a Foundry Fit Review

Foundry Advisory

£299 one-time

For deciding whether local AI makes commercial sense.

Best for: Owners, partners, MDs, and CTOs who want a clear answer before buying hardware or starting a project.

  • Workflow suitability review
  • Data-sensitivity and approval-boundary assessment
  • Hardware and model-fit recommendation
  • Current cloud AI/API spend comparison where available
  • Recommended first workflow
  • Clear yes/no on whether Foundry is worth pursuing

Outcome: A written recommendation you can take to partners, finance, IT, or compliance.

Book a Foundry Fit Review

Foundry Managed

£999 setup + £99/month support

For getting a private AI workflow running on your Mac Studio.

Best for: Businesses ready to move one or more suitable workflows local.

  • Setup on suitable Apple Silicon hardware
  • Local runtime and model profile configuration
  • Workflow harness configuration: OpenClaw or Hermes, based on need
  • Agreed workflow templates: documents, support, search, code review, or scoped automation
  • Dashboard and capacity guardrails
  • Testing with sample inputs
  • Handover for the review queue and dashboard
  • Ongoing support and tuning under the monthly support plan

Outcome: A working local AI node configured around real business work, with human approval gates and visible runtime state.

Hardware is not included. We advise on the right Apple Silicon specification if you do not already own suitable hardware.

Discuss Managed setup

Foundry On-Prem

£2,000–£5,000/month

For compliance-sensitive teams that need a defensible local AI deployment.

Best for: Legal, finance, healthcare, government-adjacent, defence-adjacent, or enterprise teams with stricter data and audit requirements.

  • On-prem deployment behind your network boundary
  • No-cloud operating mode for agreed workflows
  • Audit logging and operational runbook
  • Defined support boundaries and response expectations
  • Risk/compliance documentation support
  • Scoped support for approved internal use cases

Outcome: A managed private AI deployment your risk, IT, and compliance stakeholders can inspect and understand.

Discuss On-Prem requirements

Clear boundaries prevent expensive surprises.

  • Hardware purchase: you buy the Mac Studio or approved Apple Silicon hardware.
  • Replacement of existing business systems: email, helpdesk, CRM, case management, accounting, GitHub, and backups stay in place.
  • Open-ended custom development: new workflows are scoped separately.
  • Unsupervised legal, financial, medical, HR, or customer-facing decision-making.
  • Training your team to become local AI engineers.
  • Claims that every workload should move local.

FAQ.

Is Foundry a SaaS product?

No. Foundry is a managed private AI setup running on hardware you control. The monthly fee is for support, maintenance, updates, and tuning.

Does the data really stay local?

For configured local workflows, the AI processing runs on your Apple Silicon hardware. The deployment can be set up with no cloud AI calls for those workflows, and the dashboard can show telemetry posture. Your normal email, helpdesk, GitHub, backups, and other tools may still use their existing cloud services.

Can we use our existing Mac Studio?

Possibly. The Fit Review checks the hardware, memory, storage, workloads, and expected model profile.

Do you support Windows or Linux?

Not for the MVP offer. Foundry is positioned around Apple Silicon.

Will it replace staff?

No. Foundry is designed to reduce repetitive first-pass work and queue drafts for review. It does not replace judgement, client care, legal advice, engineering ownership, or operational accountability.

Can it run multiple workflows?

Yes, if the hardware and capacity profile support them. The Fit Review and dashboard help define what fits safely.

What if the workflow is not suitable?

We will say so. Sometimes the right answer is: keep it manual, keep it cloud, clean the data first, or start with a smaller workflow.