Partners

Partners โ€” PrivateClaw | People-powered Private AI

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PrivateClaw Partner Programme

Sell the AI your clients actually want โ€” private, owned, and done-for-you.

PrivateClaw is a per-device private AI agent that runs locally, keeps a human in charge, and is delivered through partners. We partner with any business โ€” and we build the channel around MSPs, AI agencies, solution architects and SaaS providers.

L1โ€“L3helpdesk handled on-device
24 โ†’ 5line-of-business apps consolidated
~1 : 25one device, the workload of ~25 staff
4 / 4pilots returned for a second system
Who we partner with

Built for the people who already run businesses’ technology.

PrivateClaw will partner with any company that wants to deliver private AI to its customers. Four partner types get a programme tuned to them โ€” with MSPs first, because the instant wins are on your side of the fence.

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MSPs & IT Providers

A finished, recurring-revenue AI line you can sell from day one โ€” no AI R&D, no new expertise. Wire L1โ€“L3 helpdesk straight onto the device.

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AI Agencies

A private, on-device delivery layer to build on. Package your own expert skills and deploy them into a governed, human-in-the-loop agent.

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Solution Architects

Model-agnostic, hardware-agnostic, no lock-in. Design sovereign AI into client estates with mesh VPN, per-device control and full audit.

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SaaS Providers

Add a private-AI capability to your product without sending customer data to public models. Skills provision and de-provision on demand.

+ Resellers + Consultancies + System integrators + Any company with customers to serve
Why MSPs & partners win

A new AI revenue stream that deepens the client relationship instead of competing with it.

1

Service delivery, automated

L1 to L3 ticket volumes handled faster and at higher quality than current best-in-class โ€” real engineering carried out on the client’s machine, inside an approval boundary and a full audit trail.

2

Radical consolidation

It varies, but within three months we consolidated 24 line-of-business apps down to 5 โ€” and took roughly a third of operational overhead out.

3

A genuinely new revenue stream

Private, people-centric AI solves a real client pain. We saw double-digit EBITDA growth within a month of switching it on.

4

Strong, sticky recurring revenue

Recurring income from the security stack and licensing โ€” on track for ~ยฃ35k per device in year one, with a single device handling the workload of around 25 staff.

5

Senior-engineer leverage

Autonomous “you do, I watch” learning means your best engineers are no longer the bottleneck for delivering AI services โ€” the platform learns the work and scales it.

6

Low risk, your brand, your skills

Human-in-the-loop by default, data kept local, full audit. Add your own custom skills to package bespoke, higher-value services under your name.

Done-for-you: we provide the platform, the skills and the updates. No AI research-and-development cost. No new in-house expertise required.

Early traction โ€” commercially launched

Not slideware. You can watch it run.

4 / 4pilot clients returned for a second system within two months
1000ร—growth in our own lead generation in three months
24 โ†’ 5line-of-business apps consolidated in ~3 months
~ยฃ35krecurring per device, year one (1 device โ‰ˆ 25 staff)
See it run

Agentic teams at work โ€” split by the two sides of the value.

Short clips of the platform operating live: the on-device client application, and the MSP service-delivery side. Human-in-the-loop throughout โ€” nothing acts without approval.

Client side ยท the on-device application

Local, private AI that learns the work and can assist with โ€” or autonomously run โ€” tasks on the PC, always behind approval gates. Model-agnostic, so it can always be backed by the strongest frontier model of the day.

OverviewHow the on-device agent works
Ticket handlingAgent triaging tickets on-device
Ticket handlingEnd-to-end resolution under approval
MSP side ยท service delivery

Full, secure agentic control handed to the MSP โ€” diagnose, configure, patch and remediate directly on the client’s machine, with centralised, shareable approval workflows.

Sending a quoteAgent preparing and sending a client quote
Ticket handling โ€” MSP sideService-desk automation, L1โ€“L3

Clips hosted at dashboard.adamai.co.uk. If a video doesn’t load inline, it can be opened directly from its link.

What your client gets

Daily, tangible benefit โ€” with resilience built in.

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Private by architecture

A local AI build per environment, on the customer’s own hardware. The data never leaves โ€” and we never train on it.

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Future-proof

Model-agnostic and hardware-agnostic. Plug in the strongest frontier model of the day without re-platforming. No lock-in.

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Human in charge

Approver mode set high out of the box. High-risk actions sit behind approval gates, with a full audit trail.

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Resilience & DR

Ready-made resource scaling and disaster recovery. Sickness no longer stops work; approvals are centralised and shareable.

Revenue model

Multiple recurring lines โ€” switched on centrally.

Your partner revenue lines

LineBasis
Install & onboardingOne-off โ€” your own fee (e.g. ~ยฃ1,000 / device)
Security stackRecurring, through you
Service & managementRecurring, per device & environment
Subscription marginRecurring share on PrivateClaw & skills subscriptions

PrivateClaw direct lines (for reference)

LinePrice
Server installยฃ2,000 one-off / site
Server subscriptionยฃ250 / month / site
Client subscriptionยฃ100 / month / device
Skills subscriptionGeneral + expert; add your own custom skills

A single-device site is ยฃ350/month recurring (ยฃ250 server + ยฃ100 client), plus ยฃ100/month per additional device, plus the ยฃ2,000 one-off install.

Responsible by design

Sovereignty of compute, plus an enforced human approver.

Human-in-the-loop by default. Reducing the approver is a deliberate, admin-level choice that requires written sign-off. Backed by full audit logging, least-privilege access, planned independent penetration testing, cyber insurance and a SOC-2 roadmap. We never train on customer data โ€” and by architecture, we could not.