Source material stays attached
Original documents, tickets, matter packs, knowledge-base entries, or code diffs remain connected to the draft or extraction they produced.
Managed private AI, running in your office
Local AI your team can inspect, approve, and keep improving.
Foundry turns an Apple Silicon Mac Studio into a managed private AI workflow system for documents, client support, internal knowledge, code review, and repeatable operations — with sensitive processing kept local for agreed workflows and human approval built in.
Book a Foundry Fit ReviewFollow the proof
Built for the work where confidentiality, auditability, service continuity, and human judgement matter more than AI theatre.
The pattern
It starts with one folder, one inbox, one matter pack, or one pull request.
Documents arrive. Support tickets pile up. Client files wait for missing evidence. Staff search old folders for answers. Pull requests sit in review.
Foundry watches the places work already happens, handles the first pass locally for agreed workflows, and moves the result into a review queue. Your team can see what arrived, what was extracted, what source was used, what was flagged, and what needs approval.
The data boundary gets clearer. The work moves faster. The judgement stays with your people.
Foundry is not a novelty chatbot. It is a managed local AI workflow system for repeatable work: read, classify, extract, search, draft, flag, log, and queue for review.
Local workflowactive
Source documentsvisible
Review queueopen
Human approvalrequired
Cloud AI processingnot used
Example workflow state for configured local processing.
Your own private AI node for controlled business workflows.
Foundry is installed on an Apple Silicon Mac Studio controlled by your business. It connects to selected workflows — folders, email/helpdesk systems, shared drives, case files, knowledge bases, or code repositories — and runs agreed AI processing locally.
You do not need to choose models, run inference servers, manage memory, build routing logic, or debug a local AI stack. We configure the workflow around the work you want to move local, then make the system observable, supportable, and reviewable.
Auditability by design
A private AI system should show its working.
Speed is useful only if managers can understand what happened. Foundry is designed around visible workflow state: source files, extracted fields, queue status, exceptions, draft outputs, approval checkpoints, and workflow logs.
Review queues are visible
Your team can see what is waiting, what has been flagged, what needs approval, and what has already been handled.
Approval checkpoints are explicit
Sensitive outputs can stop at a gate before they are sent, filed, relied on, escalated, or acted on.
Workflow logs create a trail
Important events can be logged: input received, workflow selected, component used, extraction produced, exception flagged, reviewer assigned, approval recorded.
For England and Wales buyers, this can support accountability and governance conversations around UK GDPR, the Data Protection Act 2018, and professional confidentiality obligations. It is not legal advice and does not guarantee compliance.
Right-sized model orchestration
Foundry does not use one AI model for everything.
Most business workflows are made of smaller steps. Some need speed. Some need careful reasoning. Some need specialist document extraction. Using one large general model for every step is often wasteful and harder to manage.
Foundry can run a managed mix of local models and workflow components on your Apple Silicon hardware, then route each step to the model or component that fits the work.
A document workflow might use OCR first, extraction second, classification third, and a larger reasoning model only for exceptions or careful summaries. A support workflow might use a small fast model for triage, source search for the right internal answer, and a larger model to draft a reply for human approval.
Small, fast models
For narrow, high-volume steps: classify, route, tag, check format, spot missing fields, and prepare work for review.
Specialist workflow components
For specific jobs such as OCR, document extraction, table reading, source matching, or structured field capture.
Larger reasoning models
For heavier work: compare documents, summarise complex matters, draft careful replies, review code, or support judgement-heavy decisions before a person approves.
Foundry routes the workflow instead of sending every step to the biggest model.
The Fit Review identifies which parts of your workflow need speed, specialist extraction, source matching, or deeper reasoning.
Workflowdocument intake
Fast modeltriage and routing
Specialist componentOCR and extraction
Larger modelsummary and exception draft
Human approvalrequired
The problems it is built for.
“We can’t put this into ChatGPT.”
Client documents, legal files, financial records, support tickets, and proprietary code often need a clearer boundary than a general cloud AI provider. Foundry moves suitable AI processing onto hardware your business controls.
“We need to know what it did.”
AI is harder to trust when outputs float away from their source. Foundry is designed to keep source material, workflow state, exceptions, drafts, and approval history visible.
“The AI bill — and the dependency — keeps growing.”
Cloud AI tools can be useful, but repeated processing through third-party APIs creates exposure to per-use costs, rate limits, repricing, model changes, service degradation, and account decisions. Foundry reduces that dependency for workloads moved local.
“Local AI sounds good until someone has to run it.”
Models change, memory runs out, endpoints drift, documents fail extraction, and dashboards are usually an afterthought. Foundry is the managed setup: model mix, routing, capacity guardrails, workflow configuration, review queues, and support.
Practical workflows, not AI theatre.
Foundry is strongest where the work is repeatable, sensitive, review-heavy, and expensive to handle manually.
Read, classify, extract, and queue documents for review.
Invoices, purchase orders, contracts, renewal notices, forms, and quotes can be processed locally. Foundry preserves the original file, extracts the key fields, flags inconsistencies, logs the workflow step, and creates a review queue.
Show what has arrived, what is missing, and what needs chasing.
For matter packs and intake files, Foundry can identify documents, check completeness, flag missing evidence, and draft chase messages for approval. It supports the admin workflow; it does not give legal advice or make professional decisions.
Draft routine replies so complex cases stop waiting.
Foundry reads tickets locally where configured, checks the knowledge base, drafts replies, routes bugs, and passes complex issues to people with context already summarised.
Ask the firm’s own documents, not the open internet.
Foundry indexes selected internal files locally and answers questions with references to source documents where possible. If it cannot find a source, it should say so.
Give every pull request a local first pass.
Foundry can review diffs for security, tests, edge cases, consistency, and obvious performance issues before a senior engineer spends their attention.
The repeated pattern is already visible.
reduction in document handling time in a structured document-processing workflow.
faster matter readiness in the conveyancing intake example.
routine support response time in the client-support workflow.
to find internal knowledge in the search example.
PR review path in the code-review workflow.
Figures are from modelled case-study workflows and demo evidence. The Fit Review confirms what is realistic for your volume, tools, data quality, hardware, model mix, and approval process.
Why it is different.
Sensitive AI processing can stay local.
For configured workflows, the AI work runs on the Mac Studio controlled by your business, reducing the need to send sensitive material to a general cloud AI provider.
The workflow leaves evidence.
Foundry can show source documents, queue status, exceptions, generated drafts, selected processing path, and approval checkpoints.
Foundry drafts. Your team decides.
Foundry does not approve payments, give legal advice, pass compliance checks, or send client-facing messages without a person in the loop.
The workflow is not welded to one model vendor.
As local models, specialist components, and Apple Silicon hardware improve, the model mix can be revisited without throwing away the workflow design, source layer, or review pattern.
More of the operating path stays under your control.
For workloads moved local, the core AI processing path is less dependent on one cloud AI account, one vendor’s pricing, one model retirement, one rate limit, one app-platform policy, or one overseas service decision.
Evidence without theatre
Owners should be able to see the state of the system.
Foundry includes operational visibility so managers do not have to trust a black box. The system can show runtime health, enabled workflows, queue status, model/component selection, capacity profile, approval state, and telemetry posture.
Modesingle-tenant local
Workflowdocument intake
Source queuevisible
Review queueopen
Approval gaterequired
Model routefast → extraction → reasoning
Capacity statussafe
Telemetry posturedisabled
Outbound AI processingnot used
Demo/runtime evidence, not a live customer deployment.
More control over the AI operating path
Your workflow should not be hostage to one cloud AI account.
Cloud AI tools are useful, but they are also external dependencies. Pricing can change. Rate limits can bite. Models can be retired. Terms and policies can move. Accounts can be reviewed or restricted. Services can degrade at the wrong time.
Foundry does not remove every dependency. You still depend on hardware, electricity, operating systems, model licences, support, local networks, and the external systems your workflow connects to. But for suitable workloads moved local, it changes the balance: the business owns more of the workflow, the review pattern, the evidence trail, and the hardware running the agreed processing path.
Find out whether local AI is worth it for your business.
Book a Foundry Fit Review. We will look at your workflows, data sensitivity, current AI spend, approval requirements, hardware position, and continuity concerns. If Foundry is a fit, you will know where to start. If it is not, we will say so.
Book a Foundry Fit ReviewSee the case studies
No pressure. No generic AI pitch. Just a practical assessment of whether private local AI can save time, reduce exposure, improve auditability, or cut recurring AI dependence in your business.