ai-six-sigma-property-os

name	ai-six-sigma-property-os
description	Design an AI Six Sigma Black Belt operating model for property service, maintenance dispatch, environmental testing, quote generation, CRM follow-up, and workflow quality dashboards. Use when the user needs a Property Agent OS, AI + Ontology + DMAIC management system, CTQ metrics, agent-team roles, work-order states, or MVP roadmap for operations quality.
version	1.0
updated	2026-06-06
assumes	The business has real or planned property service workflows with customers, work orders, workers, quotes, evidence, and quality metrics.
conflicts_with	Do not replace legal, safety, environmental compliance, payroll, tax, or customer-contract review; keep high-impact AI decisions under human confirmation.

AI Six Sigma Property OS

Build a practical operating model for property service quality using:

Ontology defines the business world.
Agent Team executes and audits workflows.
Six Sigma DMAIC continuously reduces errors, delay, rework, and cost.

This skill is for designing the management system before building software. It should produce executable operating structure: ontology, roles, CTQ metrics, work-order flow, database tables, dashboards, and MVP scope.

Usage Template

Prompt

Use ai-six-sigma-property-os for my Property Agent OS.
Design an AI + Ontology + DMAIC Black Belt operating model for property work orders, worker dispatch, environmental testing, quote generation, CRM follow-up, evidence upload, and quality dashboards.

Use Case

• Founder wants to turn messy property maintenance operations into a measurable AI workflow.
• Operator needs CTQ metrics, root-cause analysis, dispatch rules, quote controls, and evidence gates.
• Product team needs a first-stage MVP plan before building a full property SaaS.

Expected Result

• A practical operating memo with pyramid model, DMAIC loop, ontology objects, agent roles, CTQ scorecard, dashboard design, core tables, work-order states, control plan, and MVP roadmap.

Output Example

• MVP Stage 1: classify work orders, recommend workers, generate quote draft, require evidence upload, and track response time, completion time, rework rate, complaint rate, quote error, gross margin.

Verification Case

• Every module maps to at least one CTQ metric, data field, owner, human confirmation point, and control check.

Verified Effect

• A service workflow becomes a measurable quality flywheel instead of ad hoc manual coordination.

Success Metrics

• Defines the business objective and first-stage operating scope.
• Produces a DMAIC workflow tied to real property operations, not generic quality jargon.
• Names ontology objects, required fields, agent roles, CTQ metrics, dashboards, and work-order states.
• Separates AI recommendations from human approval for quotes, dispatch exceptions, safety, compliance, and customer-impacting decisions.
• Includes a narrow MVP roadmap focused on work orders, workers, quotes, evidence, and quality dashboard before expanding.

When to Use

• "Design my Property Agent OS."
• "Build an AI Six Sigma model for property maintenance."
• "Use DMAIC to improve dispatch, quote, and service quality."
• "Create CTQ metrics and dashboards for my work-order business."
• "Design agent roles for property, environmental testing, and CRM operations."

Operating Pyramid

Use this as the top-level model:

Business goals
  Reduce cost / raise speed / stabilize quality / make repeatable / support financing
      ↓
Six Sigma Black Belt layer
  DMAIC / data analysis / root cause / control plan
      ↓
Ontology semantic layer
  Customer / property / asset / work order / worker / route / quote / rule / evidence
      ↓
Agent Team execution layer
  Classify / dispatch / quote / audit / review / control
      ↓
Field operations
  Repair request / service / environmental test / payment / review

Core rule:

Ontology clarifies.
Agents execute and audit.
DMAIC improves the system after every work order.

Step 1: Define the Operating Scope

Classify the case before designing:

Field	Options
Business type	property repair, maintenance, environmental testing, cleaning, inspection, CRM follow-up
Stage	idea, manual pilot, spreadsheet MVP, internal tool, SaaS product
First workflow	work-order classification, dispatch, quote, evidence, quality dashboard
Human approval level	all decisions, quote only, exceptions only, mostly automated
Data maturity	no data, sheets, CRM, database, integrated system

Default MVP scope:

1. work-order classification
2. worker dispatch recommendation
3. quote draft generation
4. evidence upload and audit
5. quality dashboard

Do not expand into a full ERP, marketplace, payroll system, or finance system before this loop works.

Step 2: DMAIC Workflow

Map Six Sigma to the property workflow:

DMAIC	Property OS use
Define	Define customer pain, work-order types, SLA, service standards, CTQ metrics
Measure	Track response time, dispatch time, completion time, quote error, rework, complaint, evidence completeness
Analyze	Find root causes for delay, wrong dispatch, missing evidence, wrong quote, low rating
Improve	Update dispatch rules, quote rules, worker matching, SOPs, customer scripts
Control	Use dashboards, alerts, approval gates, SOP audits, agent review, weekly Black Belt review

Every work order should become a learning event:

Work order creates data
Data reveals problems
Problems trigger root-cause analysis
Root causes improve rules
Rules train agents
Agents improve speed and quality
More volume creates better data

Step 3: MECE Quality Domains

Score quality across seven non-overlapping domains:

Domain	Controls	Core metrics
Customer quality	experience, response, satisfaction	first response time, satisfaction, complaint rate
Work-order quality	classification, dispatch, completion, acceptance	first-time fix rate, rework rate, timeout rate
Worker quality	skills, location, reliability, rating	on-time rate, completion rate, customer score
Quote quality	accuracy, margin, approval	quote error rate, gross margin, close rate
Process quality	end-to-end flow	cycle time, bottleneck, wait time
Data quality	completeness, accuracy, traceability	missing field rate, missing photo rate, missing location rate
Knowledge quality	SOPs, rules, lessons	SOP hit rate, rule update frequency, case review rate

If a module has no metric, it is not ready for automation.

Step 4: Ontology Objects

Define the business world before defining agents.

Minimum ontology:

Object	Required fields
Customer	id, name, contact, address, priority, consent flags
Property	id, address, building, unit, access rules, manager
Asset	id, property_id, type, model, location, maintenance history
WorkOrder	id, customer_id, property_id, type, priority, SLA, status, description, evidence_required
Worker	id, skills, service area, location, availability, rating, capacity
Quote	id, work_order_id, labor, materials, travel, margin, approval_status
Evidence	id, work_order_id, photos, video, signature, location, timestamps, checklist
Rule	id, trigger, condition, action, owner, version
Review	id, work_order_id, issue, root_cause, countermeasure, owner, due_date

Add environmental testing fields only when needed:

Object	Fields
TestResult	work_order_id, pollutant_type, device_id, value, unit, threshold, calibration_status
Device	id, model, calibration_date, operator, status

Step 5: Agent Team Roles

Use agents as a Black Belt team, not as unbounded autonomous decision-makers.

Agent	Responsibility	Human gate
Define Agent	define problem, SLA, CTQ, work-order type	new service standard
Measure Agent	calculate metrics and missing data	metric definition changes
Analyze Agent	run 5 Why, fishbone, bottleneck analysis	root-cause acceptance
Improve Agent	propose SOP, quote, dispatch, route improvements	policy and pricing changes
Control Agent	monitor dashboard, alerts, control plan	closure of exceptions
Dispatch Agent	recommend worker by skill, distance, availability, SLA	high-risk or low-confidence dispatch
Quote Agent	draft quote and margin calculation	customer-facing quote approval
Review Agent	detect wrong orders, missing evidence, abnormal quotes, risk	case closure and disciplinary action

Step 6: CTQ Metrics

Use CTQ (Critical to Quality) as the operating scoreboard:

Goal	Metrics
Fast	first response time, dispatch time, completion time
Accurate	classification accuracy, dispatch accuracy, quote accuracy
Stable	rework rate, complaint rate, timeout rate
Cheap	cost per order, empty-run rate, material waste
Profitable	gross margin, close rate, repeat purchase
Controlled	evidence completeness, SOP execution rate, data completeness

Set thresholds:

Green = acceptable
Yellow = watch
Red = root-cause review required

Step 7: Dashboard Design

Return four dashboards:

Dashboard	Required widgets
Business	new orders today, completed orders today, revenue, gross margin, satisfaction, alerts
Process	avg response time, avg dispatch time, avg completion time, overdue orders, rework, waiting materials
Quality	classification accuracy, dispatch accuracy, quote error, evidence completeness, SOP hit rate, complaint rate
Worker	location, active orders, completed orders, on-time rate, score, empty-run rate

Every dashboard metric must trace to a table field.

Step 8: Root-Cause Analysis

For each red metric, use fishbone categories:

Category	Questions
People	wrong skill, unclear customer service note, customer unavailable
Machine	tool missing, test device failure, no system reminder
Material	stockout, wrong part, unavailable consumable
Method	unclear SOP, bad dispatch rule, quote approval too slow
Environment	route too far, weather, access restriction
Measurement	missing start time, no acceptance standard, incomplete data

Then run 5 Why until the countermeasure changes a rule, SOP, field, training item, or control threshold.

Step 9: Work-Order State Flow

Use this default state machine:

submitted
  -> classified
  -> data_needed
  -> dispatch_recommended
  -> quote_drafted
  -> quote_approved
  -> assigned
  -> in_progress
  -> evidence_uploaded
  -> quality_review
  -> customer_acceptance
  -> closed

Exception states:

waiting_customer
waiting_material
worker_rejected
quote_rejected
rework_required
cancelled
escalated

Step 10: Output Format

Return:

## AI Six Sigma Property OS Memo

Business objective:
MVP scope:
Human approval boundaries:

### Pyramid Model
...

### DMAIC Workflow
| Phase | Action | Data | Owner | Gate |

### Ontology
| Object | Required fields | Why it matters |

### CTQ Scorecard
| Goal | Metric | Formula | Green | Yellow | Red |

### Agent Team
| Agent | Input | Action | Output | Human gate |

### Work-Order State Flow
...

### Core Tables
...

### Dashboards
...

### MVP Roadmap
Phase 1:
Phase 2:
Phase 3:

### Risks and Controls
...

Quality Gates

• MVP is limited to work orders, workers, quotes, evidence, and quality dashboard unless the user explicitly asks to expand.
• Each CTQ metric has a formula or data source.
• Each agent has a bounded input, output, and human gate.
• Quote, dispatch exception, safety, compliance, customer privacy, and payment-impacting steps keep human approval.
• Root-cause analysis ends in a rule, SOP, field, training item, threshold, or owner; not just commentary.
• The output separates current facts from assumptions and missing data.