Design the system your business actually needs.

1. 1Business problem

   Leads arrive from many places, wait too long for a reply and reach the wrong person.

   Sales & lead operations · Most service businesses

2. 2Proposed architecture

   1. 1Capture and normalise the enquiry
   2. 2Enrich with known customer history
   3. 3Score intent, budget and urgency
   4. 4Route to the right person or queue
   5. 5Send a first response within minutes

   Business rules

   • Scoring thresholds
   • Territory and service routing
   • Business-hours logic

3. 3Trigger and inputs

   Triggers

   • New enquiry received

   Inputs

   • Web forms
   • Ad lead forms
   • Email enquiries
   • Chat messages

4. 4AI and software components

   • Qualification agent
   • Reply drafting agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • CRM
   • Web forms
   • Ad platforms
   • Email
   • SMS / WhatsApp

6. 6Human checkpoints

   • High-value leads reviewed before reply

7. 7Outputs

   • Qualified CRM record
   • First response
   • Owner notification
   • Response time
   • Lead source quality

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with CRM Intelligence & Pipeline Recovery, Missed-Call Recovery & Appointment System, Unified Multi-Channel Customer Inbox, Lead-to-Revenue Attribution Dashboard, Customer Reactivation & Retention Engine.

9. 9Security considerations

   • Personal data kept in the CRM, not in prompts or logs
   • Role-based access

   Failure handling

   • Low-confidence scores go to a human queue
   • Failed sends retried, then flagged

10. 10Implementation complexity

    Advanced

    • 1. Map lead sources
    • 2. Connect CRM
    • 3. Tune scoring
    • 4. Supervised launch

Targeted outcome: Faster, more consistent first responses and cleaner routing.

1. 1Business problem

   Calls missed during busy periods become lost customers.

   Sales & lead operations · Transportation & chauffeur, Professional services, Healthcare administration, Real estate

2. 2Proposed architecture

   1. 1Detect the missed call
   2. 2Send an instant text-back
   3. 3Collect the reason for calling
   4. 4Offer available appointment slots
   5. 5Confirm and log the booking

   Business rules

   • Opening hours
   • Do-not-contact list
   • Slot rules

3. 3Trigger and inputs

   Triggers

   • Missed or after-hours call

   Inputs

   • Call logs
   • Caller number
   • Calendar availability

4. 4AI and software components

   • Conversational intake agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Business phone system
   • SMS
   • Calendar
   • CRM

6. 6Human checkpoints

   • Urgent or complex requests passed to staff

7. 7Outputs

   • Booked appointment
   • Call-back task
   • Recovered calls
   • Booking conversion

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Lead Qualification & Routing Engine, Intelligent Quote-to-Booking Pipeline, Chauffeur Booking & Dispatch Automation, Unified Multi-Channel Customer Inbox.

9. 9Security considerations

   • Consent-aware messaging
   • Phone numbers masked in reports

   Failure handling

   • Double-booking checks
   • Fallback to a call-back task

10. 10Implementation complexity

    Moderate

    • 1. Phone system review
    • 2. Text-back flow
    • 3. Calendar sync
    • 4. Launch

Targeted outcome: Fewer lost callers and more booked appointments.

1. 1Business problem

   Messages are spread across email, WhatsApp, Instagram and web chat.

   Sales & lead operations · Most service businesses

2. 2Proposed architecture

   1. 1Pull messages into one queue
   2. 2Match each message to a customer
   3. 3Classify intent and urgency
   4. 4Suggest a reply
   5. 5Track until resolved

   Business rules

   • Assignment rules
   • Response targets

3. 3Trigger and inputs

   Triggers

   • New message on any channel

   Inputs

   • Email
   • WhatsApp
   • Social DMs
   • Web chat

4. 4AI and software components

   • Intent classifier
   • Reply suggestion agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Email
   • WhatsApp Business
   • Social inboxes
   • CRM

6. 6Human checkpoints

   • Suggested replies sent by staff

7. 7Outputs

   • Single conversation history
   • Assigned tasks
   • Response and resolution times

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Lead Qualification & Routing Engine, Internal Knowledge & SOP Assistant, Review & Reputation Management Workflow, Missed-Call Recovery & Appointment System.

9. 9Security considerations

   • Channel permissions respected
   • Message retention policy

   Failure handling

   • Unmatched contacts flagged
   • Channel outages queued

10. 10Implementation complexity

    Advanced

    • 1. Channel audit
    • 2. Connect inboxes
    • 3. Classification
    • 4. Team rollout

Targeted outcome: One place to see and answer every customer conversation.

1. 1Business problem

   Quotes take too long to prepare and rarely get followed up.

   Sales & lead operations · Transportation & chauffeur, Travel & hospitality, Professional services, Warehousing & logistics

2. 2Proposed architecture

   1. 1Validate the request details
   2. 2Draft a quote from pricing rules
   3. 3Approve and send
   4. 4Follow up automatically
   5. 5Convert to a confirmed booking

   Business rules

   • Pricing and surcharge rules
   • Quote expiry

3. 3Trigger and inputs

   Triggers

   • Quote requested

   Inputs

   • Quote request form
   • Pricing rules
   • Availability

4. 4AI and software components

   • Quote drafting agent
   • Follow-up agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Website forms
   • CRM
   • Email
   • Payment links
   • Calendar

6. 6Human checkpoints

   • Quotes approved before sending
   • Discounts need sign-off

7. 7Outputs

   • Sent quote
   • Deposit link
   • Booking record
   • Quote-to-booking rate
   • Time to quote

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Missed-Call Recovery & Appointment System, Chauffeur Booking & Dispatch Automation, Invoice Follow-up & Receivables Workflow, AI Lead Qualification & Routing Engine.

9. 9Security considerations

   • Payment handled by the payment provider only

   Failure handling

   • Missing details trigger a clarification message
   • Expired quotes archived

10. 10Implementation complexity

    Advanced

    • 1. Pricing rules
    • 2. Quote templates
    • 3. Payment links
    • 4. Launch

    Builds on the quote request flow used on a client website.

Targeted outcome: Quicker quotes and fewer bookings lost to silence.

1. 1Business problem

   Deals stall in the CRM and nobody notices until it's too late.

   Sales & lead operations · Most service businesses

2. 2Proposed architecture

   1. 1Scan the pipeline for stalled deals
   2. 2Summarise each deal's history
   3. 3Suggest the next best action
   4. 4Create tasks for owners

   Business rules

   • Stall thresholds by stage

3. 3Trigger and inputs

   Triggers

   • Daily pipeline scan
   • Deal inactive for set days

   Inputs

   • CRM deals
   • Activity history
   • Email engagement

4. 4AI and software components

   • Deal summarisation agent
   • Next-action agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • CRM
   • Email
   • Task manager

6. 6Human checkpoints

   • Owners approve outreach

7. 7Outputs

   • Owner tasks
   • Recovery emails (draft)
   • Pipeline health
   • Recovered deals

8. 8Dependencies

   Requires AI Lead Qualification & Routing Engine.

   Works well with AI Lead Qualification & Routing Engine, Customer Reactivation & Retention Engine, Lead-to-Revenue Attribution Dashboard, Executive Daily Operations Briefing.

9. 9Security considerations

   • Read-only CRM access where possible

   Failure handling

   • Incomplete records listed for cleanup

10. 10Implementation complexity

    Advanced

    • 1. CRM data audit
    • 2. Rules
    • 3. Agent setup
    • 4. Review cadence

Targeted outcome: A pipeline that tells you where attention is needed.

1. 1Business problem

   Past customers drift away without a reason to come back.

   Sales & lead operations · Most service businesses

2. 2Proposed architecture

   1. 1Segment customers by history
   2. 2Pick a relevant offer or reminder
   3. 3Personalise the message
   4. 4Send on the right channel
   5. 5Track replies and bookings

   Business rules

   • Frequency caps
   • Unsubscribe rules

3. 3Trigger and inputs

   Triggers

   • Customer inactive for set period
   • Seasonal date

   Inputs

   • Customer history
   • Purchase or booking dates
   • Preferences

4. 4AI and software components

   • Segmentation agent
   • Personalisation agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • CRM
   • Email platform
   • SMS / WhatsApp

6. 6Human checkpoints

   • Campaigns approved before sending

7. 7Outputs

   • Personalised campaigns
   • Re-engaged leads
   • Reactivation rate

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with CRM Intelligence & Pipeline Recovery, Review & Reputation Management Workflow, AI Lead Qualification & Routing Engine, AI Marketing Operations Control Centre.

9. 9Security considerations

   • Consent and unsubscribe respected
   • Anti-spam compliance (CASL)

   Failure handling

   • Bounces and opt-outs cleaned automatically

10. 10Implementation complexity

    Moderate

    • 1. Segments
    • 2. Message library
    • 3. Approval flow
    • 4. Launch

Targeted outcome: More repeat business from customers you already have.

1. 1Business problem

   Campaigns, content and budgets are managed in separate places.

   Marketing & growth · Most service businesses

2. 2Proposed architecture

   1. 1Pull spend and results into one view
   2. 2Flag under-performing campaigns
   3. 3Suggest budget and creative changes
   4. 4Queue approved changes

   Business rules

   • Budget limits
   • Pacing alerts

3. 3Trigger and inputs

   Triggers

   • Daily sync
   • Budget threshold reached

   Inputs

   • Ad accounts
   • Content calendar
   • Budgets
   • Analytics

4. 4AI and software components

   • Performance analyst agent
   • Creative brief agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Meta Ads
   • Google Ads
   • Analytics
   • Content calendar

6. 6Human checkpoints

   • Budget changes approved by a person

7. 7Outputs

   • Action list
   • Creative briefs
   • Spend vs results by channel

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Lead-to-Revenue Attribution Dashboard, Automated Campaign Monitoring & Reporting, Multi-Brand Content Operations System, Customer Reactivation & Retention Engine.

9. 9Security considerations

   • Least-privilege ad account access

   Failure handling

   • API sync failures alerted

10. 10Implementation complexity

    Complex

    • 1. Account access
    • 2. Data model
    • 3. Alerts
    • 4. Weekly review

Targeted outcome: One place to steer marketing spend and content.

1. 1Business problem

   It's unclear which campaigns actually produce paying customers.

   Marketing & growth · Most service businesses

2. 2Proposed architecture

   1. 1Tag every lead with its source
   2. 2Connect leads to deals and revenue
   3. 3Combine with ad spend
   4. 4Show cost per customer by channel

   Business rules

   • Attribution model
   • Data freshness checks

3. 3Trigger and inputs

   Triggers

   • New lead or sale recorded

   Inputs

   • UTM data
   • CRM deals
   • Ad spend
   • Bookings or sales

4. 4AI and software components

   • Anomaly explanation agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Analytics
   • CRM
   • Ad platforms
   • Accounting or POS

6. 6Human checkpoints

   • None required

7. 7Outputs

   • Attribution dashboard
   • Cost per lead
   • Cost per customer
   • Revenue by source

8. 8Dependencies

   Requires AI Lead Qualification & Routing Engine.

   Works well with AI Lead Qualification & Routing Engine, AI Marketing Operations Control Centre, Automated Campaign Monitoring & Reporting, CRM Intelligence & Pipeline Recovery.

9. 9Security considerations

   • Aggregated reporting, no personal data on dashboards

   Failure handling

   • Untracked leads flagged
   • Sync gaps shown on the dashboard

10. 10Implementation complexity

    Advanced

    • 1. Tracking audit
    • 2. CRM link-up
    • 3. Dashboard
    • 4. Review

Targeted outcome: Clear evidence of which marketing pays for itself.

1. 1Business problem

   Producing on-brand content for several brands or channels is slow.

   Marketing & growth · Marketing agency, Travel & hospitality, Retail & e-commerce

2. 2Proposed architecture

   1. 1Turn briefs into drafts per brand
   2. 2Check against brand rules
   3. 3Route for review
   4. 4Schedule approved posts

   Business rules

   • Brand voice rules
   • Posting windows

3. 3Trigger and inputs

   Triggers

   • Calendar slot due
   • New campaign brief

   Inputs

   • Brand guidelines
   • Content calendar
   • Asset library

4. 4AI and software components

   • Drafting agent
   • Brand compliance checker

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Content calendar
   • Design tools
   • Social schedulers
   • Asset storage

6. 6Human checkpoints

   • Every post approved before publishing

7. 7Outputs

   • Scheduled content
   • Asset records
   • Output by brand
   • Approval turnaround

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Marketing Operations Control Centre, Automated Client Onboarding Workspace, Agency Client Reporting Portal, Review & Reputation Management Workflow.

9. 9Security considerations

   • Separate access per brand

   Failure handling

   • Failed posts retried and flagged

10. 10Implementation complexity

    Advanced

    • 1. Brand rules
    • 2. Templates
    • 3. Approval flow
    • 4. Scheduling

Targeted outcome: More consistent content with less manual production.

1. 1Business problem

   Campaign problems are noticed days late, after budget is wasted.

   Marketing & growth · Most service businesses

2. 2Proposed architecture

   1. 1Check delivery, spend and results
   2. 2Compare against targets
   3. 3Explain what changed
   4. 4Send alerts and weekly summaries

   Business rules

   • Alert thresholds

3. 3Trigger and inputs

   Triggers

   • Hourly or daily checks

   Inputs

   • Ad platform data
   • Analytics
   • Targets

4. 4AI and software components

   • Change explanation agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Meta Ads
   • Google Ads
   • Analytics
   • Email or Slack

6. 6Human checkpoints

   • None required

7. 7Outputs

   • Alerts
   • Weekly report
   • Performance vs target

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Marketing Operations Control Centre, Lead-to-Revenue Attribution Dashboard, Agency Client Reporting Portal.

9. 9Security considerations

   • Read-only access to ad accounts

   Failure handling

   • Missing data reported, not hidden

10. 10Implementation complexity

    Moderate

    • 1. Targets
    • 2. Data connections
    • 3. Alerts
    • 4. Summaries

Targeted outcome: Problems spotted early instead of at month end.

1. 1Business problem

   Building client reports by hand takes hours every month.

   Marketing & growth · Marketing agency, Professional services

2. 2Proposed architecture

   1. 1Collect results per client
   2. 2Write a plain-language summary
   3. 3Review internally
   4. 4Publish to the client portal

   Business rules

   • Client-specific KPIs

3. 3Trigger and inputs

   Triggers

   • Reporting period ends

   Inputs

   • Campaign data
   • Deliverables log
   • Client goals

4. 4AI and software components

   • Report writer agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Ad platforms
   • Analytics
   • Client portal
   • Email

6. 6Human checkpoints

   • Account manager approves each report

7. 7Outputs

   • Client report
   • Portal update
   • Report delivery times

8. 8Dependencies

   Requires Automated Campaign Monitoring & Reporting.

   Works well with Automated Campaign Monitoring & Reporting, Multi-Brand Content Operations System, Automated Client Onboarding Workspace.

9. 9Security considerations

   • Client data isolated per account

   Failure handling

   • Missing sources noted in the report

10. 10Implementation complexity

    Advanced

    • 1. KPI templates
    • 2. Data connections
    • 3. Portal
    • 4. Review workflow

Targeted outcome: Reports that arrive on time and read clearly.

1. 1Business problem

   Happy customers rarely leave reviews and unhappy ones go unanswered.

   Marketing & growth · Most service businesses

2. 2Proposed architecture

   1. 1Ask for a review at the right moment
   2. 2Monitor new reviews
   3. 3Draft responses
   4. 4Escalate negative feedback

   Business rules

   • Timing rules
   • One request per customer

3. 3Trigger and inputs

   Triggers

   • Job completed
   • New review posted

   Inputs

   • Completed jobs or bookings
   • Review platforms

4. 4AI and software components

   • Response drafting agent
   • Sentiment classifier

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Google Business Profile
   • CRM
   • SMS / Email

6. 6Human checkpoints

   • Responses to negative reviews approved

7. 7Outputs

   • Review requests
   • Published responses
   • Review volume and rating trend

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Customer Reactivation & Retention Engine, Unified Multi-Channel Customer Inbox, Chauffeur Booking & Dispatch Automation.

9. 9Security considerations

   • No incentives that break platform rules

   Failure handling

   • Unmatched reviews logged for manual reply

10. 10Implementation complexity

    Moderate

    • 1. Platforms
    • 2. Request timing
    • 3. Response templates
    • 4. Launch

Targeted outcome: A steadier flow of reviews and faster replies.

1. 1Business problem

   Staff re-type information from forms, IDs and PDFs.

   Administration & operations · Professional services, Healthcare administration, Real estate, Warehousing & logistics

2. 2Proposed architecture

   1. 1Classify the document
   2. 2Extract the key fields
   3. 3Validate against rules
   4. 4Route exceptions for review
   5. 5Save to the right record

   Business rules

   • Required fields
   • Format checks

3. 3Trigger and inputs

   Triggers

   • Document uploaded or received

   Inputs

   • Uploaded PDFs
   • Scanned forms
   • Email attachments

4. 4AI and software components

   • Document classifier
   • Field extraction model

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Document storage
   • CRM or practice system
   • Email

6. 6Human checkpoints

   • Low-confidence extractions reviewed by staff

7. 7Outputs

   • Structured records
   • Exception queue
   • Volume processed
   • Exception rate

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Automated Client Onboarding Workspace, Professional Services Client Intake & Routing, Veterinary Administrative Workflow System, Internal Knowledge & SOP Assistant.

9. 9Security considerations

   • Encrypted storage
   • Access logging
   • Retention rules for sensitive documents

   Failure handling

   • Unreadable files returned with a request to resend

10. 10Implementation complexity

    Advanced

    • 1. Document inventory
    • 2. Extraction tests
    • 3. Validation rules
    • 4. Supervised launch

Targeted outcome: Less re-typing and fewer data-entry errors.

1. 1Business problem

   Every new client needs the same forms, folders, accounts and emails.

   Administration & operations · Marketing agency, Professional services

2. 2Proposed architecture

   1. 1Create folders and project space
   2. 2Send welcome pack and forms
   3. 3Collect access and assets
   4. 4Schedule the kickoff
   5. 5Hand over to the delivery team

   Business rules

   • Package-specific checklists

3. 3Trigger and inputs

   Triggers

   • Deal marked won

   Inputs

   • Signed agreement
   • Client details
   • Service package

4. 4AI and software components

   • Checklist agent
   • Missing-info chaser

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • CRM
   • Project management
   • Document storage
   • Calendar
   • Email

6. 6Human checkpoints

   • Account lead confirms before kickoff

7. 7Outputs

   • Ready workspace
   • Kickoff meeting
   • Time to kickoff

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Multi-Brand Content Operations System, Agency Client Reporting Portal, AI Document Intake & Validation, Invoice Follow-up & Receivables Workflow.

9. 9Security considerations

   • Credentials collected through a secure vault, never by email

   Failure handling

   • Stalled steps escalated after set days

10. 10Implementation complexity

    Moderate

    • 1. Checklist design
    • 2. Templates
    • 3. Automation
    • 4. Launch

Targeted outcome: A consistent first week for every client.

1. 1Business problem

   Chasing unpaid invoices is awkward and easy to forget.

   Administration & operations · Most service businesses

2. 2Proposed architecture

   1. 1Send friendly reminders
   2. 2Escalate tone on a schedule
   3. 3Offer a payment link
   4. 4Notify staff for persistent overdue accounts

   Business rules

   • Reminder schedule
   • Customer exclusions

3. 3Trigger and inputs

   Triggers

   • Invoice due soon
   • Invoice overdue

   Inputs

   • Accounting invoices
   • Due dates
   • Payment status

4. 4AI and software components

   • Reminder personalisation

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Accounting software
   • Email
   • Payment links

6. 6Human checkpoints

   • Final notices approved

7. 7Outputs

   • Reminders
   • Overdue list
   • Days sales outstanding
   • Overdue balance

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Intelligent Quote-to-Booking Pipeline, Automated Client Onboarding Workspace, Executive Daily Operations Briefing.

9. 9Security considerations

   • Payment details stay with the payment provider

   Failure handling

   • Disputed invoices paused automatically

10. 10Implementation complexity

    Moderate

    • 1. Accounting link
    • 2. Templates
    • 3. Schedule
    • 4. Launch

Targeted outcome: Fewer late payments with less awkward chasing.

1. 1Business problem

   Staff can't find procedures and keep asking the same questions.

   Administration & operations · Most service businesses

2. 2Proposed architecture

   1. 1Index approved documents
   2. 2Answer with sources
   3. 3Flag gaps in documentation
   4. 4Suggest SOP updates

   Business rules

   • Answer only from approved sources

3. 3Trigger and inputs

   Triggers

   • Staff question asked

   Inputs

   • SOPs
   • Policies
   • Help docs
   • Past answers

4. 4AI and software components

   • Retrieval assistant (RAG)

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Document storage
   • Team chat

6. 6Human checkpoints

   • SOP changes approved by an owner

7. 7Outputs

   • Sourced answers
   • Documentation gap list
   • Top questions
   • Unanswered topics

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Unified Multi-Channel Customer Inbox, AI Document Intake & Validation, Professional Services Client Intake & Routing, Executive Daily Operations Briefing.

9. 9Security considerations

   • Permission-aware search
   • No training on private data

   Failure handling

   • Uncertain answers say so and point to a person

10. 10Implementation complexity

    Advanced

    • 1. Document cleanup
    • 2. Indexing
    • 3. Pilot team
    • 4. Rollout

Targeted outcome: Faster answers and procedures people actually use.

1. 1Business problem

   Owners start the day without a clear view of what needs attention.

   Administration & operations · Most service businesses

2. 2Proposed architecture

   1. 1Collect overnight changes
   2. 2Highlight risks and priorities
   3. 3Write a short briefing
   4. 4Deliver by email or chat

   Business rules

   • Priority thresholds

3. 3Trigger and inputs

   Triggers

   • Every morning

   Inputs

   • Bookings
   • Pipeline
   • Finance
   • Support queue

4. 4AI and software components

   • Briefing writer agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • CRM
   • Calendar
   • Accounting
   • Email or chat

6. 6Human checkpoints

   • None required

7. 7Outputs

   • Daily briefing
   • Trends over time

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with CRM Intelligence & Pipeline Recovery, Invoice Follow-up & Receivables Workflow, Chauffeur Booking & Dispatch Automation, Warehouse Operations Intelligence Dashboard.

9. 9Security considerations

   • Sensitive figures limited to owners

   Failure handling

   • Missing sources listed in the briefing

10. 10Implementation complexity

    Moderate

    • 1. Choose metrics
    • 2. Connect sources
    • 3. Briefing format
    • 4. Launch

Targeted outcome: A five-minute read that replaces checking six tools.

1. 1Business problem

   Data is copied between apps by hand and gets out of sync.

   Administration & operations · Most service businesses

2. 2Proposed architecture

   1. 1Listen for changes
   2. 2Map fields between apps
   3. 3Apply business rules
   4. 4Update the other systems
   5. 5Log every step

   Business rules

   • Field mappings
   • Conflict resolution

3. 3Trigger and inputs

   Triggers

   • Record created or updated in any app

   Inputs

   • Events from connected apps

4. 4AI and software components

   • Data cleanup assistant

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Automation platform or custom API layer
   • CRM
   • Accounting
   • Forms

6. 6Human checkpoints

   • Bulk changes approved

7. 7Outputs

   • Systems kept in sync
   • Audit log
   • Sync volume
   • Failures

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Lead Qualification & Routing Engine, Automated Client Onboarding Workspace, Invoice Follow-up & Receivables Workflow, Warehouse Operations Intelligence Dashboard.

9. 9Security considerations

   • Scoped API keys
   • Audit trail

   Failure handling

   • Retries with backoff
   • Dead-letter queue for failed events

10. 10Implementation complexity

    Complex

    • 1. System map
    • 2. Data contracts
    • 3. Build and test
    • 4. Monitor

Targeted outcome: One source of truth instead of copy-and-paste.

1. 1Business problem

   Bookings, driver assignment and confirmations are coordinated by hand.

   Industry systems · Transportation & chauffeur

2. 2Proposed architecture

   1. 1Confirm booking details
   2. 2Assign driver and vehicle
   3. 3Send confirmations and reminders
   4. 4Track flight changes
   5. 5Close out with receipt and review request

   Business rules

   • Airport wait-time rules
   • Multi-stop pricing
   • Cancellation policy

3. 3Trigger and inputs

   Triggers

   • Booking confirmed
   • Flight time changes

   Inputs

   • Booking requests
   • Flight details
   • Driver availability
   • Vehicle list

4. 4AI and software components

   • Booking detail checker
   • Message drafting

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Booking form
   • Dispatch app or calendar
   • SMS / WhatsApp
   • Payments

6. 6Human checkpoints

   • Dispatcher approves assignments

7. 7Outputs

   • Driver job sheet
   • Customer confirmations
   • Receipt
   • Trips per day
   • On-time rate

8. 8Dependencies

   Requires Intelligent Quote-to-Booking Pipeline.

   Works well with Missed-Call Recovery & Appointment System, Intelligent Quote-to-Booking Pipeline, Review & Reputation Management Workflow, Executive Daily Operations Briefing.

9. 9Security considerations

   • Customer contact details shared with assigned driver only

   Failure handling

   • Unassigned jobs escalated before pickup

10. 10Implementation complexity

    Complex

    • 1. Process mapping
    • 2. Booking data
    • 3. Dispatch rules
    • 4. Supervised launch

    Informed by hands-on booking and confirmation work for a chauffeur client.

Targeted outcome: Smoother bookings with fewer manual messages.

1. 1Business problem

   Stock levels, orders and shipments live in separate systems.

   Industry systems · Warehousing & logistics, Retail & e-commerce

2. 2Proposed architecture

   1. 1Sync stock and order data
   2. 2Detect low stock and delays
   3. 3Forecast simple demand trends
   4. 4Alert the right team

   Business rules

   • Reorder points
   • Delay thresholds

3. 3Trigger and inputs

   Triggers

   • Scheduled sync
   • Stock threshold crossed

   Inputs

   • WMS or inventory data
   • Orders
   • Shipments

4. 4AI and software components

   • Anomaly and trend agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Inventory or WMS
   • E-commerce platform
   • Carrier tracking
   • Chat

6. 6Human checkpoints

   • Purchase orders approved by a manager

7. 7Outputs

   • Operations dashboard
   • Alerts
   • Stock health
   • Fulfilment times

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Cross-Application Workflow Orchestrator, Executive Daily Operations Briefing, Unified Multi-Channel Customer Inbox.

9. 9Security considerations

   • Read-only connections to core systems

   Failure handling

   • Stale data clearly marked

10. 10Implementation complexity

    Complex

    • 1. Data sources
    • 2. Model
    • 3. Dashboard
    • 4. Alerts

Targeted outcome: Earlier warning on stock and fulfilment problems.

1. 1Business problem

   Agents juggle listing enquiries and viewing schedules by phone.

   Industry systems · Real estate

2. 2Proposed architecture

   1. 1Answer listing questions
   2. 2Pre-qualify the buyer or renter
   3. 3Offer viewing slots
   4. 4Send reminders
   5. 5Collect feedback after the viewing

   Business rules

   • Viewing windows
   • Qualification criteria

3. 3Trigger and inputs

   Triggers

   • Listing enquiry received

   Inputs

   • Listing enquiries
   • Agent calendars
   • Listing data

4. 4AI and software components

   • Listing Q&A agent
   • Qualification agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Listing portals
   • Calendar
   • CRM
   • SMS

6. 6Human checkpoints

   • Agent confirms viewings

7. 7Outputs

   • Booked viewings
   • Feedback notes
   • Enquiry-to-viewing rate

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Lead Qualification & Routing Engine, Missed-Call Recovery & Appointment System, AI Document Intake & Validation.

9. 9Security considerations

   • Applicant data handled under privacy rules

   Failure handling

   • Cancelled viewings re-offered

10. 10Implementation complexity

    Advanced

    • 1. Listing data
    • 2. Calendar
    • 3. Agent pilot
    • 4. Launch

Targeted outcome: More viewings booked with less back-and-forth.

1. 1Business problem

   New enquiries arrive incomplete and reach the wrong specialist.

   Industry systems · Professional services, Marketing agency

2. 2Proposed architecture

   1. 1Guide the client through intake
   2. 2Summarise the request
   3. 3Route to the right specialist
   4. 4Book a consultation

   Business rules

   • Service routing
   • Conflict checks

3. 3Trigger and inputs

   Triggers

   • Intake submitted

   Inputs

   • Intake form
   • Service catalogue
   • Team availability

4. 4AI and software components

   • Intake summariser
   • Routing classifier

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Website forms
   • CRM
   • Calendar
   • Email

6. 6Human checkpoints

   • Specialist accepts the referral

7. 7Outputs

   • Routed enquiry
   • Consultation booking
   • Intake volume by service

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with AI Document Intake & Validation, Internal Knowledge & SOP Assistant, Automated Client Onboarding Workspace, Missed-Call Recovery & Appointment System.

9. 9Security considerations

   • Confidential intake data encrypted

   Failure handling

   • Unclear requests go to a coordinator

10. 10Implementation complexity

    Moderate

    • 1. Intake design
    • 2. Routing rules
    • 3. Calendar
    • 4. Launch

    A simplified version runs on this site: Build Your Stack summarises a visitor's needs and hands them to 6ixOnline's enquiry form.

Targeted outcome: Complete enquiries that reach the right person first time.

1. 1Business problem

   Clinic staff spend hours on reminders, intake forms and records.

   Industry systems · Healthcare administration

2. 2Proposed architecture

   1. 1Send pre-visit forms
   2. 2Remind owners of due care
   3. 3Prepare the visit summary for staff
   4. 4Send aftercare instructions

   Business rules

   • Care schedules
   • Clinic hours

3. 3Trigger and inputs

   Triggers

   • Appointment booked
   • Vaccine or check-up due

   Inputs

   • Appointment book
   • Patient records
   • Vaccination schedules

4. 4AI and software components

   • Form summariser
   • Reminder personalisation

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Practice management system
   • SMS / Email
   • Forms

6. 6Human checkpoints

   • Clinical messages approved by staff

7. 7Outputs

   • Completed intake
   • Reminders
   • Aftercare messages
   • No-show rate
   • Reminder response

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Missed-Call Recovery & Appointment System, AI Document Intake & Validation, Review & Reputation Management Workflow.

9. 9Security considerations

   • No medical advice from automation
   • Records access limited by role

   Failure handling

   • Failed reminders retried, then called

10. 10Implementation complexity

    Advanced

    • 1. System access
    • 2. Forms
    • 3. Reminder schedules
    • 4. Pilot

Targeted outcome: Less admin time per visit and fewer missed appointments.

1. 1Business problem

   Turning travel enquiries into itineraries and bookings is slow.

   Industry systems · Travel & hospitality

2. 2Proposed architecture

   1. 1Capture dates, group and budget
   2. 2Draft a matching itinerary
   3. 3Agent reviews and prices
   4. 4Send proposal and follow up
   5. 5Convert to booking and documents

   Business rules

   • Supplier availability
   • Deposit rules

3. 3Trigger and inputs

   Triggers

   • Travel enquiry received

   Inputs

   • Enquiry details
   • Tour inventory
   • Supplier rates

4. 4AI and software components

   • Itinerary drafting agent
   • Follow-up agent

   Model choice (Claude, OpenAI, Grok through the xAI API, or a self-hosted open-weight model) is made during discovery, based on the data involved, cost and accuracy needs.

5. 5Integrations

   • Website forms
   • Tour inventory
   • CRM
   • Email
   • Payments

6. 6Human checkpoints

   • Agent approves every itinerary

7. 7Outputs

   • Itinerary proposal
   • Booking confirmation
   • Enquiry-to-booking rate

8. 8Dependencies

   Runs on its own; no other module is required.

   Works well with Intelligent Quote-to-Booking Pipeline, Customer Reactivation & Retention Engine, Multi-Brand Content Operations System, Unified Multi-Channel Customer Inbox.

9. 9Security considerations

   • Passport and payment data kept out of AI prompts

   Failure handling

   • Unavailable tours swapped for agent review

10. 10Implementation complexity

    Complex

    • 1. Inventory data
    • 2. Itinerary templates
    • 3. Agent workflow
    • 4. Launch

Targeted outcome: Faster proposals and more consistent follow-up.