MWMS Client AI Interface Selection Framework

Document Type: Framework
Status: Draft For MCR
Version: v1.0
Authority: MWMS HeadOffice
Parent Page: HeadOffice
Applies To: AIBS Brain, Automation Brain, Product Brain, Sales Brain, Operations Brain, Customer Brain, AI Employees, AI Operating Systems, future client-facing AI systems
Last Reviewed: 2026-05-31
Source / Origin: AI Automations by Jack — Advanced Technology Section
MWMS Classification: Client Interface Selection Framework / AIOS Interface Governance Standard / AIBS Client Delivery Design Layer
Primary Brain: AIBS Brain
Supporting Brains: HeadOffice Brain, Automation Brain, Product Brain, Sales Brain, Operations Brain, Customer Brain, Risk Brain, Compliance Brain, Data Brain, SIT Brain, Content Brain
Related Pages: MWMS Advanced AI Capability Stack Framework, AIBS Brain Canon, Automation Brain Canon, MWMS AI Operating System Architecture Framework, MWMS Automation Client Demo And Handover Framework, MWMS Automation Build Planning Framework, MWMS AI Tool Permission And Access Framework, MWMS AI Agent Memory And Context Framework, MWMS AI Agent Operations Core, MWMS Context Engineering Framework, MWMS AI Automation Security And Risk Checklist, HeadOffice Kaizen Continuous Improvement Loop
Source Evidence: AI Automations by Jack introduces several client-facing and operator-facing AI interface options including Custom GPTs, AI app builders, voice AI systems, chatbots, Chrome extensions, AI dashboards, and advanced n8n systems. The course explains Custom GPTs as an accessible way to create shareable assistants with instructions and knowledge; voice systems as digital or phone-based agents; chatbots as text interfaces that become powerful with memory, tools, knowledge, and handoff; and Chrome extensions as browser copilots that can send webpage data to automation workflows.

Purpose

The MWMS Client AI Interface Selection Framework defines how MWMS chooses the correct AI interface for internal systems, client-facing systems, AIBS packages, AI Operating Systems, prototypes, demos, and future productized services.

This framework exists because AI systems can now appear through many different interfaces.

A system may be presented as:

a Custom GPT
a chatbot
a voice agent
a dashboard
a web app
a Chrome extension
a form workflow
a WordPress page
a GoHighLevel interface
an internal admin panel
a browser copilot
an AI receptionist
a client portal
a report generator

The interface matters.

The wrong interface can make a useful system confusing.

The right interface can make a simple automation feel valuable, understandable, and easy to use.

MWMS must therefore choose the interface based on the user, problem, risk, workflow, ownership model, data sensitivity, and business outcome.

The goal is not to use the most advanced interface.

The goal is to use the most appropriate interface.

Strategic Importance

This framework is strategically important because AIBS will eventually package AI systems for businesses.

Those businesses will not buy “n8n workflows,” “Make scenarios,” “webhooks,” or “AI agents” as technical objects.

They will buy:

faster lead response
better customer support
smoother onboarding
clearer reporting
reduced admin
better internal knowledge access
improved appointment handling
stronger follow-up
better sales support
easier decision-making

The interface is how the client experiences that value.

A strong backend with the wrong interface can feel weak.

A simple backend with the right interface can feel powerful.

This framework helps MWMS avoid interface drift.

Core Doctrine

The MWMS doctrine is:

Choose the interface that best delivers the business outcome to the user.

The interface should be selected after answering:

Who is the user?
What are they trying to do?
Where are they already working?
What action should they take?
What information do they need?
How much control should they have?
How much risk is involved?
Does the system need to talk, chat, show, capture, report, or act?
Is the system internal, client-facing, customer-facing, or public?

The interface must serve the workflow.

The workflow must serve the outcome.

The outcome must serve the business.

Definition

A client AI interface is the visible or interactive layer through which a user experiences, controls, reviews, or receives value from an AI system.

It may be:

conversational
visual
voice-based
form-based
dashboard-based
browser-based
app-based
report-based
embedded
internal
client-facing
customer-facing

MWMS Definition

A Client AI Interface is:

The user-facing layer of an AI Operating System that allows a person to input information, receive output, review work, approve action, view reporting, or interact with an AI Employee.

Interface Is Not The System

An interface is not the whole system.

A Custom GPT is not the full AIOS.

A chatbot is not the full AIOS.

A voice agent is not the full AIOS.

A Chrome extension is not the full AIOS.

A dashboard is not the full AIOS.

A web app is not the full AIOS.

Each interface is only one layer.

A serious AI Operating System may include:

interface layer
AI reasoning layer
automation layer
data layer
memory/context layer
reporting layer
governance layer
support layer
improvement loop

MWMS Rule

Do not confuse the interface with the operating system.

1. Custom GPT Interface

A Custom GPT is a lightweight assistant interface built inside ChatGPT using instructions, knowledge, examples, and tool settings.

The course presents Custom GPTs as one of the most accessible ways to create something unique and shareable with other people using custom knowledge.

Best For

Use a Custom GPT when the need is:

quick assistant prototype
internal knowledge helper
training support
simple content helper
brainstorming helper
repeatable prompt workflow
shareable early demo
low-risk guided assistant
temporary specialist helper

MWMS Examples

YouTube Title Helper
Offer Evaluation Draft Helper
Course Absorption Prompt Helper
Sales Call Prep Helper
Brand Voice Helper
Client FAQ Draft Helper
Simple SOP Assistant

Strengths

fast to create
easy to share
low build complexity
good for knowledge packaging
good for internal testing
useful for early client demos
requires little infrastructure

Weaknesses

limited operational control
weak logging compared to MWMS systems
limited workflow orchestration
limited approval gates
weak database integration
weak role permission governance
not ideal for client-grade production
not ideal for complex automations

Risk Level

Low to medium.

Risk increases when:

client data is uploaded
public claims are created
sensitive knowledge is included
users mistake it for a fully governed system
it is shared too widely

Use Custom GPT When

speed matters more than infrastructure
the task is low-risk
the output is advisory or draft
a simple knowledge assistant is enough
no deep workflow integration is required

Do Not Use Custom GPT When

the workflow requires database writes
the system needs logs and audit trail
client data isolation matters
task routing matters
external actions matter
approval gates matter
production reliability is required

MWMS Rule

A Custom GPT is a fast assistant layer, not a production AIOS.

2. Chatbot Interface

A chatbot is a text-based conversational interface that allows a user to ask questions, receive guidance, and potentially trigger tools or workflows.

The course explains that chatbots become more valuable when they have memory, tools, knowledge, databases, and human handoff logic.

Best For

Use a chatbot when the need is:

text-based interaction
customer questions
internal knowledge support
lead qualification
onboarding guidance
support triage
FAQ navigation
sales inquiry handling
guided workflow assistance
internal team assistant

MWMS Examples

Website Lead Qualification Chatbot
Client FAQ Bot
AIBS Internal Knowledge Bot
Support Triage Bot
Brain Room Assistant
Course Library Assistant
Sales Objection Assistant
Client Onboarding Chatbot

Strengths

familiar user experience
easy to embed on websites
good for Q&A
good for support
can collect lead data
can connect to tools
can use RAG and memory
can route or escalate

Weaknesses

can hallucinate
needs updated knowledge
customer-facing risk
poor interface for complex visual reporting
not ideal for users who need structured dashboards
can frustrate users without handoff
may need careful prompt and memory governance

Risk Level

Medium to high depending on use.

Risk increases when:

it is customer-facing
it answers compliance-sensitive questions
it collects personal data
it triggers tools
it lacks human handoff
it uses stale knowledge

Use Chatbot When

conversation is useful
the user has questions
knowledge lookup is important
guided support is needed
natural language intake is useful
handoff is available

Do Not Use Chatbot When

the user needs structured reporting
the output is better shown visually
the task is simple form capture
the user needs high trust and exact data
a dashboard or form would be clearer
the system cannot safely answer customer questions

Required Governance

A client-facing chatbot should define:

knowledge source
short-term memory
long-term memory
tool access
prohibited topics
handoff trigger
escalation path
logging
data capture rules
update process
compliance boundaries

MWMS Rule

A chatbot is useful only when it knows what it knows, knows what it can do, and knows when to hand off.

3. Voice AI Interface

A voice AI interface allows users or customers to interact with an AI system through spoken conversation.

The course explains that voice matters because it transfers information faster than typing and maps strongly to real business communication. It separates voice systems into digital website agents and phone-based inbound/outbound systems.

Best For

Use voice AI when the need is:

phone interaction
appointment confirmation
reception
customer inquiry handling
spoken onboarding
hands-free support
quick data capture
human-like first response
accessibility
conversational website guidance

MWMS Examples

AI Receptionist
Appointment Confirmation Agent
Lead Qualification Voice Agent
Website Voice Guide
Client Onboarding Voice Assistant
Internal Learning Companion
Sales Call Preparation Voice Assistant
Post-Call Summary Agent

Strengths

natural communication
faster than typing
strong for phone-based businesses
high perceived value
useful for support and booking
can improve accessibility
can integrate with knowledge and tools

Weaknesses

higher customer-facing risk
harder to review in real time
needs strong script boundaries
requires handoff
compliance/consent issues
call recording rules may apply
poor answers can damage trust quickly
voice quality and latency matter

Risk Level

High for client-facing or customer-facing use.

Risk is lower for internal voice assistants.

Use Voice AI When

the business already relies on calls
speed of communication matters
customers prefer talking
appointment or intake workflows are common
a clear script and handoff path exist
call logging and review are possible

Do Not Use Voice AI When

compliance rules are unclear
the system lacks escalation
the business cannot tolerate bad answers
the knowledge source is weak
the use case requires exact visual data
the client is not ready for AI voice handling

Required Governance

Voice AI must define:

script boundaries
knowledge sources
human transfer path
call logging
consent/recording rules
escalation rules
prohibited claims
fallback responses
client approval
test cases
monitoring process

MWMS Rule

Voice AI should not face customers until handoff, consent, knowledge, logging, and escalation are governed.

4. Dashboard Interface

A dashboard interface shows structured information, status, reports, metrics, outputs, tasks, or decisions visually.

The Advanced Technology intro identifies AI-powered dashboards as a major future automation capability for 2025/2026, especially for displaying live data to clients and integrating with almost anything.

Best For

Use dashboards when the need is:

status visibility
recurring reports
KPI tracking
decision support
task review
approval queues
client proof of value
operational monitoring
system health
management oversight

MWMS Examples

HeadOffice Dashboard
Client AIOS Dashboard
Newsletter Intelligence Dashboard
Lead Qualification Dashboard
AIBS Client Value Report
Automation Health Dashboard
Experimentation Results Dashboard
Content Pipeline Dashboard

Strengths

makes value visible
supports recurring revenue proof
easier for managers
good for structured data
good for approvals
good for monitoring
reduces black-box feeling
supports client trust

Weaknesses

can be overbuilt
needs clean data
needs metric definitions
can become dashboard noise
not ideal for deep conversation
poor dashboards weaken trust
stale data is dangerous

Risk Level

Medium.

Risk increases when:

data is inaccurate
client decisions depend on it
financial metrics are shown
compliance data is involved
dashboard implies certainty without evidence

Use Dashboard When

recurring value must be shown
users need visibility
outputs need approval
data should be tracked over time
client retention depends on proof
management needs oversight

Do Not Use Dashboard When

the workflow is not validated
data source is unreliable
metrics are unclear
the client does not need recurring visibility
a simple email/report is enough

Required Governance

Dashboards should define:

data source
refresh cadence
metric definitions
owner
action rules
confidence/validation status
source visibility
stale data warning
user permissions

MWMS Rule

A dashboard should prove value and support decisions, not become decoration.

5. Web App Interface

A web app interface is a custom or semi-custom application built for users to input, process, review, or receive AI-powered outputs.

The course shows AI app builders and app-generation workflows as a way to create functioning apps and prototypes quickly, especially using visual references, code references, and APIs.

Best For

Use a web app when the need is:

custom workflow
structured interface
repeatable user action
user account or portal
multi-step process
visual output
app-like experience
client demonstration
lead magnet tool
internal system interface

MWMS Examples

Offer Evaluation App
Client Intake App
AI Report Generator
Lead Scoring Tool
Prompt Vault Interface
AIOS Launch Review App
Content Brief Generator
Experiment Registry Micro-App

Strengths

highly flexible
strong demo value
supports structured workflows
can connect to APIs
can become productized
good for client-facing systems
can support dashboards and forms
more controlled than chat for some tasks

Weaknesses

can be overbuilt
generated code may be fragile
requires security review
needs maintenance
needs ownership
can create false production confidence
may need M/developer hardening

Risk Level

Medium to high.

Risk increases when:

it touches client data
it uses live APIs
users depend on it
authentication is needed
payments or sensitive workflows are involved
generated code is deployed without review

Use Web App When

the workflow needs structure
the user needs buttons, forms, outputs, or visual steps
chat is too vague
dashboard alone is not interactive enough
prototype validation matters
client demo needs polish

Do Not Use Web App When

a simple form is enough
a dashboard is enough
a Custom GPT is enough
the workflow is not validated
security cannot be reviewed
maintenance ownership is unclear

Required Governance

A web app should define:

user roles
authentication needs
data storage
tool/API access
error handling
deployment model
ownership
maintenance
testing
client handover
privacy/security rules

MWMS Rule

AI app builders can create prototypes fast, but production apps require governance and hardening.

6. Chrome Extension / Browser Copilot Interface

A Chrome extension or browser copilot sits inside the browser and can interact with web pages, capture content, add controls, or send data to workflows.

The course demonstrates a Chrome extension that captures webpage content and sends it to an n8n webhook.

Best For

Use browser copilots when the need is:

webpage capture
research intake
prompt saving
competitor analysis
YouTube summarization
transcript extraction
ad library review
sales page capture
browser-side workflow launch
manual-to-automated bridge

MWMS Examples

Prompt Vault Chrome Extension
Send To Research Brain
Send Sales Page To Affiliate Brain
Send YouTube Transcript To Content Brain
Save Prompt To Supabase
Capture Competitor Landing Page
Send Page To n8n Analysis Workflow

Strengths

works where the user already browses
high internal productivity value
can capture live web context
can trigger automations quickly
useful for research workflows
supports Prompt Vault direction
can become a browser-based AI copilot

Weaknesses

browser permission risk
data exposure risk
needs careful destination control
can send sensitive webpage data
can be fragile across websites
needs extension maintenance
security risk if poorly built

Risk Level

Medium to high.

Risk increases when:

it captures private pages
it sends data to external services
it accesses logged-in pages
it handles client data
it captures credentials
webhook destination is unclear

Use Browser Copilot When

work begins inside the browser
webpage data is central
one-click capture improves workflow
the destination is governed
data capture rules are clear
internal use comes before client use

Do Not Use Browser Copilot When

data sensitivity is unclear
destination is not defined
permissions are too broad
users may capture private content accidentally
governance is not ready

Required Governance

Browser copilots should define:

page data captured
page data excluded
destination workflow
webhook security
user confirmation
data logging
sensitive data warning
user permission
internal/client boundary
storage rules

MWMS Rule

Browser copilots are powerful intake tools, but they must not become uncontrolled data capture tools.

7. Form Interface

A form interface collects structured input from a user and sends it into an AI or automation workflow.

Forms may be built in:

Typeform
Tally
Google Forms
WordPress
Carrd
GoHighLevel
custom web apps
n8n forms
Make forms

Best For

Use forms when the need is:

structured intake
lead qualification
onboarding
support request collection
client setup data
campaign intake
product research request
automation request
repeatable internal submission

MWMS Examples

Client Intake Form
AIBS Discovery Form
Offer Evaluation Intake Form
Newsletter Submission Form
Prompt Vault Submission Form
Automation Request Form
Course Upload Intake Form

Strengths

controlled input
easier automation
good for structured workflows
reduces chatbot ambiguity
easy to validate
good for lead qualification
good for onboarding

Weaknesses

less flexible than chat
can feel rigid
poor fit for exploratory questions
may need follow-up logic
users may skip fields or enter bad data

Risk Level

Low to medium.

Risk increases when sensitive data is collected.

Use Form When

structured input matters
required fields matter
automation depends on clean data
the process is repeatable
chat would be too vague

Do Not Use Form When

conversation is needed
user needs guidance
input is highly variable
emotional or complex support is involved

Required Governance

Forms should define:

required fields
optional fields
validation rules
privacy notice
data destination
approval rules
automation trigger
error handling
follow-up path

MWMS Rule

Use forms when clean input matters more than conversational flexibility.

8. Report Interface

A report interface delivers AI output as a structured document, email, PDF, page, dashboard card, or recurring summary.

Best For

Use reports when the need is:

decision support
periodic insight
client value proof
management summary
audit trail
handoff
strategic review
compliance note
test result summary
research synthesis

MWMS Examples

Weekly HeadOffice Report
Client AIOS Monthly Value Report
Offer Evaluation Report
Experiment Result Report
Newsletter Intelligence Digest
Course Absorption Closeout Report
Automation Health Report

Strengths

good for decision-making
easy to review
good for client proof
supports accountability
can be archived
useful for recurring service
strong for executive summaries

Weaknesses

not interactive
may become stale
can be too long
weak if not action-oriented
poor for live operations

Risk Level

Low to medium.

Risk increases when:

report affects financial decisions
client-facing claims are included
data is uncertain
compliance-sensitive conclusions are made

Use Report When

the user needs a decision summary
recurring proof matters
human review is required
output should be archived
the system needs a formal record

Do Not Use Report When

live interaction is required
the user needs workflow control
real-time status matters
the user needs to act inside the system

Required Governance

Reports should define:

source data
period covered
owner
output format
confidence level
recommendations
next actions
approval status
archive destination

MWMS Rule

A report should help someone decide or act.

If yes, apply:

client demo/handover rules
risk review
compliance review
support model
human handoff
logging
source visibility

Interface Selection Matrix

Interface	Best For	Avoid When	Risk Level
Custom GPT	Lightweight assistant, internal helper, demo	Production workflow, logging, client data	Low-Medium
Chatbot	Q&A, lead/support conversation	Structured reporting, no handoff	Medium-High
Voice AI	Calls, receptionist, spoken intake	Compliance unclear, no handoff	High
Dashboard	Visibility, reporting, proof of value	Data unreliable, workflow unvalidated	Medium
Web App	Custom workflow, structured controls	Simple tasks, no maintenance owner	Medium-High
Chrome Extension	Browser capture, research intake	Sensitive pages, unclear destination	Medium-High
Form	Structured intake	Exploratory conversation needed	Low-Medium
Report	Decision summary, recurring proof	Live action/control required	Low-Medium

Off-The-Shelf Versus Custom Interface Rule

Not every AI interface should be custom.

The course makes the useful point that custom chatbots are not always the best route; sometimes an off-the-shelf chatbot is easier, cleaner, and better for standardized use cases.

Use Off-The-Shelf When

the use case is common
client needs simple FAQ/support
speed matters
budget is low
handover must be simple
client already uses the tool
customization is not critical
support burden should be low

Use Custom When

the workflow is unique
tool access is specific
design matters
database-backed memory is needed
AIOS integration is needed
custom routing is needed
client package differentiation matters
standard tools cannot handle the process

MWMS Rule

Custom is only better when it creates better business value, better system fit, or better control.

Internal Versus Client-Facing Interface Rule

Internal interfaces can tolerate more rough edges.

Client-facing interfaces cannot.

Internal Interface May Be

rougher
manual-reviewed
experimental
fast-changing
used for learning
less polished
lower support burden

Client-Facing Interface Must Be

clear
stable
tested
explainable
branded where needed
supportable
permissioned
logged where needed
privacy-aware
handover-ready
monitored

MWMS Rule

Do not show a client an interface that MWMS cannot explain, support, or govern.

Interface And AIOS Layer Mapping

Interfaces map into the AIOS stack.

Interface	AIOS Layer
Custom GPT	AI Reasoning / Lightweight Interface
Chatbot	Conversational Interface / Context Layer
Voice AI	Voice Interface / Customer Interaction Layer
Dashboard	Reporting / Interface Layer
Web App	Front-End / Workflow Interface
Chrome Extension	Browser Intake / Tool Interface
Form	Structured Intake Layer
Report	Reporting / Decision Layer

MWMS Rule

Every interface must have a defined AIOS layer and system role.

Client Interface Governance Requirements

Client-facing interfaces require governance.

Before client use, define:

user type
interface purpose
business outcome
data collected
data displayed
AI role
tool access
memory rules
human approval
escalation
handoff
logging
support owner
update process
maintenance model
privacy/compliance considerations
client training
handover documentation

MWMS Rule

Correction:

Restrict to internal or demo mode until governed.

Minimum Useful Interface Rule

MWMS should choose the simplest interface that creates the required value.

A minimum useful interface may be:

form + email output
simple dashboard
Custom GPT
chatbot with handoff
internal web app prototype
Chrome extension for one capture task
report generator

Do not jump to full apps, voice agents, or complex dashboards when simpler interfaces prove value faster.

MWMS Rule

Start with the minimum useful interface, then expand only when usage proves need.

AIBS Application

AIBS should use this framework to package AI systems around client use cases.

Possible AIBS interface packages:

Lead Qualification Chatbot
AI Receptionist
Client Onboarding Portal
Appointment Confirmation Voice Agent
Weekly AI Business Report Dashboard
Internal Knowledge GPT
Sales Page Review Browser Copilot
Customer Support Triage Bot
Client Intake Form-To-Report System
AIOS Management Dashboard

AIBS must define:

interface type
user type
client outcome
data flow
tool access
ownership model
maintenance model
handover model
recurring value proof

AIBS Rule

The interface should make the AIBS value easy to understand, use, and renew.

Sales Brain Application

Sales Brain should use interface selection to sell outcomes clearly.

Instead of selling:

“AI chatbot”
“Custom GPT”
“voice agent”
“Chrome extension”
“AI dashboard”

Sell:

“Lead response system”
“Appointment recovery assistant”
“Customer support triage system”
“Weekly management visibility dashboard”
“Sales page review copilot”
“Client onboarding assistant”

Sales Rule

Name the interface by the business outcome, not the technology.

Product Brain Application

Product Brain should prevent interface bloat.

Before approving an interface, ask:

Is this the simplest useful interface?
Does the user need this?
Is this polish or function?
Does this improve adoption?
Does this improve retention?
Does this create support burden?
Does this delay a more important build?
Can this be deferred?

Product Rule

Interface design should improve usability, not create unnecessary product complexity.

Risk And Compliance Application

Risk Brain and Compliance Brain must review interfaces that:

face customers
collect personal data
provide advice
make claims
process sensitive information
trigger external action
use voice or phone calls
capture browser content
display financial or performance data
operate in regulated contexts

Risk Rule

The more direct the interface is to customers, the stronger the guardrails must be.

Related AI Employee Capabilities

Interface Selection Agent

Chooses the correct interface based on user, workflow, risk, and business outcome.

Client Interface Risk Reviewer

Checks customer-facing risk, compliance exposure, handoff, and data capture.

AIOS Interface Mapper

Maps the interface to the full AI Operating System stack.

Client Demo Interface Designer

Designs the demo surface and wow moment for client presentation.

Chatbot Handoff Designer

Defines chatbot escalation, human handoff, and fallback logic.

Voice AI Governance Reviewer

Reviews voice scripts, consent, escalation, and customer-facing risk.

Browser Copilot Intake Designer

Defines what page data can be captured and where it goes.

Future Expansion

This framework may later produce:

MWMS Chatbot Memory And Handoff Standard
MWMS Voice AI Governance Framework
MWMS AI Dashboard Interface Standard
MWMS Browser Copilot And Chrome Extension Framework
MWMS Custom GPT Usage Standard
MWMS AI App Prototype Governance Standard
MWMS Client AIOS Interface Template
MWMS Client Interface Risk Checklist

Create these only when the relevant capability becomes an active constraint.

Strategic Summary

The Advanced Technology section shows that MWMS has many possible AI interfaces available.

The strategic lesson is not to use all of them.

The lesson is to choose deliberately.

Custom GPTs are good for quick assistants.

Chatbots are good for conversational support when memory, tools, and handoff are governed.

Voice AI is powerful but high-risk and needs strong controls.

Dashboards show recurring value and support client retention.

Web apps support structured workflows and polished demos.

Chrome extensions can become powerful browser copilots but need data capture governance.

Forms remain excellent for structured input.

Reports remain excellent for decision support and value proof.

MWMS should choose interfaces based on user need, business outcome, risk, and system maturity.

Final Standard

The MWMS standard is:

Choose the interface that best delivers the business outcome to the user.
Do not confuse interface with system.
Use the minimum useful interface first.
Govern before client use.
Add complexity only when the workflow proves it needs it.

The best AI interface is not the most impressive one.

The best AI interface is the one the user understands, trusts, uses, and continues to value.

Change Log

Version: v1.0
Date: 2026-05-31
Author: MWMS HeadOffice

Change:

Created the MWMS Client AI Interface Selection Framework from AI Automations by Jack — Advanced Technology Section.

Captured the course’s advanced interface categories including Custom GPTs, chatbots, voice AI systems, AI app builders, Chrome extensions, AI dashboards, and browser copilots.

Defined interface selection rules for Custom GPTs, Chatbots, Voice AI, Dashboards, Web Apps, Chrome Extensions / Browser Copilots, Forms, and Reports.

Added Interface Selection Decision Tree and Interface Selection Matrix.

Added Off-The-Shelf Versus Custom Interface Rule based on the course’s chatbot guidance that custom is not always the best path and simple standardized chatbot needs may be better handled with off-the-shelf tools.

Added Internal Versus Client-Facing Interface Rule.

Mapped interface types to AIOS layers.

Added Client Interface Governance Requirements, Interface Failure Modes, and Minimum Useful Interface Rule.

Defined AIBS, Sales Brain, Product Brain, Risk Brain, and Compliance Brain applications.

Added related AI Employee capabilities: Interface Selection Agent, Client Interface Risk Reviewer, AIOS Interface Mapper, Client Demo Interface Designer, Chatbot Handoff Designer, Voice AI Governance Reviewer, and Browser Copilot Intake Designer.

Purpose of creation:

To give MWMS a governed method for choosing the correct AI interface for internal systems, client demos, AIBS packages, AI Operating Systems, and future client-facing delivery without confusing the interface with the full system.