Apple Retail OS

What are we working on?

Build governed apps, documents, and workflows with your organization's context.

Starter workspaces

Shop, Pickup, Support & SessionsExplore an illustrative public journey across shopping, Apple Pickup, support, and Today at Apple sessions.

Live App

Store & Digital Journey ReadinessDraft a public-source readiness brief spanning shopping, stores, pickup, support, and sessions.

Document

Privacy-First Responsible AI CandidatesCompare hypothetical retail-assistance candidates through privacy, accessibility, grounding, and human-review gates.

Slide Deck

Illustrative prototype. Uses public company context and simulated data; no connection to Apple Retail systems.Independent illustrative prototype; not authorized, sponsored, or endorsed by Apple Inc.

Build an illustrative public retail journey from the listed Apple pages. Map shop and store discovery, Apple Pickup, support, and Today at Apple session discovery using only synthetic scenario data.

AI

I start with no access. I'll request only the evidence needed for this workspace.

Gatekeeper: Public Web + Synthetic Scenarios
Read-only access to listed public Apple pages and synthetic scenario records. No Apple systems, telemetry, customer data, orders, appointments, credentials, or employee data are available.

Approve read-only access.

AI

Executed deterministic query

const pages = await env.PUBLIC_WEB.getPages({
  paths: ["retail", "pickup", "support", "sessions"]
});
const scenarios = await env.SYNTHETIC_SCENARIOS.list({
  dataset: "public-retail-journey"
});
app.render(PublicRetailJourney, { pages, scenarios });

The illustrative journey is ready. Messages, reservations, purchases, and support actions remain outside the prototype.

Create a store and digital journey readiness brief using only public Apple pages and synthetic test scenarios. Do not infer architecture, programs, priorities, service levels, or performance.

AI

I'll use curated company context and request only the resources needed for this workspace.

Gatekeeper: Approved Public Sources
Read-only access to the listed source URLs and synthetic test scenarios. No Apple systems, analytics, customer records, orders, reservations, or employee information are included.

AI

The brief is ready as a hypothetical test plan and makes no claim about architecture, priorities, or performance at Apple.

Create a privacy-first responsible AI candidate review grounded only in public shopping, pickup, support, and Today at Apple journeys. Treat every use case and score as hypothetical.

AI

I'll use curated company context and request only the resources needed for this workspace.

Gatekeeper: Public Context + Synthetic Evaluations
Read-only access to public Apple pages and synthetic candidate and evaluation records. No customer records, prompts, Apple systems, or production model information are available.

AI

Executed deterministic query

const candidates = await env.DEMO_CANDIDATES.list({
  scope: "public-retail", status: ["candidate", "explore"]
});
const evals = await env.SYNTHETIC_EVALS.summary({
  dimensions: ["grounding", "privacy", "accessibility", "human-oversight"]
});
deck.render(PrivacyFirstReview, { candidates, evals });

The candidate review is ready. Every concept remains hypothetical, advisory, evaluation-gated, and subject to human approval.

Shop, Pickup, Support & Sessions

Illustrative data. Public-source prototype only. No Apple systems or data are connected; all counts, alerts, and readiness percentages are fictional.

4

Illustrative moments

3

Illustrative handoffs

2

Illustrative review flags

0

Apple systems connected

Attention queue

Review: A fictional pickup scenario needs human confirmation before any customer communication.

Monitor: A synthetic session result needs location and accessibility checks before display.

Illustrative readiness from synthetic scenarios

Shop and store discovery

92%

Test

Pickup notification and handoff

81%

Review

Support preparation

74%

Review

Session discovery

88%

Test

Policy follows the data. Sharing this app re-checks the viewer against every observed resource.

Illustrative policy trace

Step	Enforced policy	Status
Request resource	Starts with no access; request is limited to Public Web + Synthetic Scenarios	Approved read-only
Run deterministic query	Typed capability; credential remains isolated	Logged
Render app	Observed resources stay attached to the output	Bound
Share	Viewer permissions are checked at open time	Human controlled

Store & Digital Journey Readiness

Illustrative planning artifact. Hypothetical planning artifact based only on public Apple pages; not an Apple plan, architecture description, or performance report.

Apple Retail - Store & Digital Journey Readiness

Public-source test plan | Illustrative draft

Purpose

Define testable readiness checks for the public journey from product and store discovery through pickup, support, and Today at Apple session discovery without inferring internal systems.

Jobs to be done

Priority	Journey moment	Required review
Shopping continuity	Browse, compare, and find a store	Content + accessibility review
Pickup readiness	Notification and handoff	Privacy + service review
Support preparation	Find help or prepare for a visit	Privacy + human review
Session discovery	Choose a store and session	Accessibility + content review

Operating principles

Start with a measurable job to be done, not a new tool.
Use curated company context before model knowledge.
The human owner remains accountable for every output.
An agent never receives more permission than the person using it.

Gatekeepers hold credentials, scope every resource, and preserve the observation trail when work is shared.

Delivery sequence

Map: Capture only durable promises stated on public Apple pages.

Test: Exercise synthetic journeys across device sizes, locations, and accessibility needs.

Review: Require human privacy, accessibility, and content approval before demonstration.

Control alignment

Use only approved public sources and synthetic scenarios. Treat data minimization, accessibility, payment and identity safeguards, source grounding, retention limits, and human approval as design requirements.

Privacy-First Responsible AI Candidates

Slide 1 of 4

Privacy-First Responsible AI Candidates

Apple retail journey | Independent illustrative prototype

Slide 2 of 4

Public-context opportunity areas

Use case	Stage	Human owner	Next gate
Public-catalog shopping guidance	Candidate	Experience reviewer	Public sources + grounding tests
Pickup and support preparation	Explore	Service reviewer	Minimize data + confirm
Accessible session discovery	Candidate	Accessibility reviewer	Test access + avoid profiling

Hypothetical candidates only. No Apple initiative, deployment, model use, architecture, or performance is asserted; every scorecard value is illustrative.

Slide 3 of 4

Governance scorecard

3

Illustrative candidates

4

Evaluation dimensions

0

Customer records

100%

Human-review target

Illustrative target-state controls.

Slide 4 of 4

Next operating loops

Context: curate terminology, policies, and quality criteria.

Evaluation: define task-specific quality, safety, and fairness tests.

Access: bind every data resource through a Gatekeeper.

Efficiency: use code for deterministic work and models only for judgment.

Integrations

Organization-wide connections for Apple Retail OS. Gatekeepers hold credentials, scope resources, and log each action.

Prototype catalog. Connections and authorization states are simulated.

Gatekeepers

1

Productivity suite

Mail, calendar, documents, spreadsheets, and files

2

Collaboration

Chat, channels, meetings, and workflow notifications

3

Project tracking

Programs, epics, issues, sprints, and delivery status

4

Knowledge base

Policies, procedures, standards, and team documentation

5

Service management

IT tickets, incidents, change requests, and asset data

6

HRIS

Employee directory, organization, benefits, and lifecycle workflows

7

ERP & procurement

Finance, planning, purchasing, supply chain, and billing

8

CRM

Customer, account, partner, and service relationship data

9

Code platform

Repositories, reviews, issues, and engineering standards

10

Data platform

Governed warehouse, catalog, analytics, and reporting

11

Security operations

Alerts, cases, exposure, audit, and control evidence

12

Business intelligence

Dashboards, semantic models, and executive reporting

MCP Server Portals

Illustrative remote services available to authorized workspaces.

Security Operations

https://security.mcp.demo.example/mcp

Auto

Enterprise Data Catalog

https://data.mcp.demo.example/mcp

Needs auth

Finance & Procurement

https://finance.mcp.demo.example/mcp

Auto

People Directory

https://people.mcp.demo.example/mcp

Needs auth

Cloudflare API

https://cloudflare.mcp.demo.example/mcp

Auto

Organization Context

Shared, curated knowledge that grounds every Apple Retail OS workspace. Context is versioned and read-only to agents.

Public operating context: apple.com · Internal-looking documents below are illustrative.

md

company-strategy.md

Mission, operating model, annual priorities, and outcome definitions

md

brand-and-communications.md

Terminology, voice, accessibility, and approved communication patterns

md

security-standards.md

Identity, data protection, secure development, and incident requirements

md

responsible-ai-standard.md

AI risk tiers, evaluations, human oversight, and acceptable use

md

data-classification.md

Data categories, handling rules, retention, and sharing restrictions

md

architecture-principles.md

Technology standards, decision records, review criteria, and ownership

md

vendor-risk.md

Due diligence, contract controls, monitoring, and exit requirements

md

customer-experience.md

Journey definitions, service standards, and quality measures

md

operations-playbook.md

Service ownership, runbooks, escalation, continuity, and recovery

md

finance-controls.md

Planning, purchasing, expense, audit, and reporting procedures

md

people-policies.md

Hiring, onboarding, performance, leave, and workplace guidance

md

legal-and-compliance.md

Review paths, records, privacy, accessibility, and regulatory obligations

Skills

Reusable workflows for every function. The human requester owns the result.

Name	Description	Group	Source
meeting-prep	Build an agenda and briefing from authorized calendar, CRM, and document context	General	Shared library
weekly-operating-review	Create a cross-functional summary with decisions, owners, and open risks	General	Shared library
incident-response	Assemble evidence, draft updates, and preserve human approval for containment	Security	Shared library
vendor-risk-review	Compare due-diligence evidence with security and privacy standards	Security	Shared library
control-evidence-pack	Map authorized evidence to control requirements and identify gaps	Security	Shared library
architecture-review	Review a proposal against architecture principles and decision criteria	IT & Architecture	Shared library
change-impact	Map dependencies, affected services, stakeholders, and rollback requirements	IT & Architecture	Shared library
service-health-review	Summarize service levels, incidents, changes, and capacity risks	Operations	Shared library
runbook-builder	Turn a procedure into a deterministic workflow with approval gates	Operations	Shared library
ai-model-review	Summarize ownership, evaluations, drift, risk tier, and release readiness	Data & AI	Shared library
data-quality-report	Assess freshness, completeness, lineage, and policy compliance	Data & AI	Shared library
budget-variance	Compare actuals with plan and draft a finance-reviewed variance narrative	Finance	Shared library
procurement-brief	Summarize requirements, alternatives, risk, and approval status	Finance	Shared library
job-description	Draft an accessible role description from approved job architecture	HR	Shared library
onboarding-plan	Create a role-based onboarding plan without expanding system permissions	HR	Shared library
contract-intake	Extract terms, route issues, and prepare a legal review checklist	Legal	Shared library
privacy-assessment	Map a proposed workflow to data categories and privacy obligations	Legal	Shared library
account-brief	Create a customer briefing from authorized CRM and public information	Sales	Shared library
proposal-draft	Build a first draft using approved claims, pricing, and brand context	Sales	Shared library
executive-update	Turn project evidence into a concise decision-oriented update	General	Shared library

Profile

Illustrative account information for this public prototype.

Account

AI Gateway

Illustrative demo data. Visibility and controls across every AI provider Apple Retail uses — one console.

Requests

128,400

▲ 11% vs last mo

Tokens

342M

▲ 8% vs last mo

Est. spend

$9,120

76% of budget

Cache-hit

27%

▲ saves ~$2.4k

Error rate

0.6%

▼ 0.2 pts

p50 latency

480 ms

across providers

Models in Use

This month

Model	Route	Tokens	Spend	Share	p50 latency
Llama 3.3 70B	Workers AI	156M	$2,140	42%	310 ms
Claude	via AI Gateway	98M	$3,980	24%	720 ms
GPT-4o	via AI Gateway	61M	$2,510	18%	640 ms
Workers AI embeddings (bge)	Workers AI	27M	$190	16%	40 ms

Spend vs. Budget

9 days remaining

$9,120spent of $12,000 cap

76%

On track · ~$2,880 left with 9 days

Top Users

Demo User 0142M tok $1,180

Demo User 0231M tok $960

Demo User 0328M tok $840

Demo User 0422M tok $610

Usage by Workspace / Team

342M tokens total

AI Enablement

121M tokens · $3,240

Platform Engineering

89M tokens · $2,460

Customer Experience

62M tokens · $1,510

Enterprise Operations

41M tokens · $1,020

Security & Compliance

29M tokens · $890

Governance

Guardrails enforced by Gatekeepers + AI Gateway, with resource-scoped access, audit trails, and human approval.

Per-team allowed models

Restrict which providers each workspace can call.

Monthly spend caps

Hard limits per team; agents stop before overrun.

Data & AI $4,000Platform $3,000

PII redaction

Strip sensitive fields from prompts before they leave.

Enabled

Prompt / response logging

Full request logs retained for audit & review.

Enabled · 90-day retention

Rate limits

Per-team request ceilings to protect budgets.

600 req / min|burst 1,000

Raise Data & AI cap to $6,000

Change queued by an agent — needs a human sign-off.

Requires approval

What are we working on?

Illustrative policy trace

Resource boundaries

Apple Retail - Store & Digital Journey Readiness

Purpose

Jobs to be done

Operating principles

Delivery sequence

Control alignment

Workspace sources

Resource boundaries

Privacy-First Responsible AI Candidates

Public-context opportunity areas

Governance scorecard

Next operating loops

Workspace sources

Resource boundaries

Integrations

Organization Context

Skills

Profile

AI Gateway

Models in Use

Spend vs. Budget

Usage by Workspace / Team

Governance

Per-team allowed models

Monthly spend caps

PII redaction

Prompt / response logging

Rate limits

Raise Data & AI cap to $6,000

AI Gateway Explorer

Review spend cap change