ai-six-sigma-property-os

# AI Six Sigma Property OS

Build a practical operating model for property service quality using:

```text
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**
```text
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:

```text
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:

```text
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:

```text
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:

```text
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