# Outcome Contract — Forge PM 100,000 Work Order Simulation

> **FICTIONAL / SYNTHETIC DEPLOYMENT MODEL.** Forge Property Management is an
> invented enterprise customer. No real customer data was used.

## What this outcome is

A scaled, synthetic enterprise simulation that runs the **existing, verified
Work Order Agent Ecosystem** (`delivery-package/work-order-agents`) against a
deterministic corpus of **100,000** property-management work orders modeled on a
fictional 42,000-unit portfolio, then quantifies operational performance and a
transparent ROI versus a fully-manual baseline.

## What this outcome is NOT

- It is **not** a real customer deployment or case study.
- It does **not** use real tenant, property, vendor, or financial data.
- It does **not** claim realized production results or `PRODUCTION_VALIDATED`
  status.

## Scope (in)

1. **Synthetic dataset** — 100,000 labeled work orders with unit/property/region,
   trade category, priority, tenant description, timestamp, duplicate patterns,
   missing-field cases, emergency cases, vendor candidates, estimated cost, SLA
   requirement, and the expected classification/routing/validation outcome.
2. **Simulation runner** — boots the real ecosystem (PostgreSQL via PGlite + gRPC
   dispatch) and processes all 100,000 orders through classify → route → validate
   → action, measuring twelve operational metrics.
3. **ROI model** — manual vs. agent-assisted labor economics from stated
   assumptions, with full arithmetic.
4. **Executive report** — a PDF-ready impact study.
5. **Web case study page** — a public portfolio page with the synthetic disclosure.
6. **Social package** — marketing copy that labels itself a fictional simulation.
7. **Proof package** — dataset method, assumptions, ROI sheet, simulation results,
   audit trace, checksum manifest, limitations, and verification report.

## Disclosed seams

- Fictional customer; synthetic data; simulated operational + financial assumptions.
- Live infrastructure is in-process (PGlite + loopback gRPC); external Postgres and
  a Go gRPC service are wire-compatible but not exercised here.
- The classifier is a deterministic lexicon engine, not a hosted LLM.

## Success criteria (MUST_PASS, asserted by `verify.mjs`)

- All 100,000 orders processed through the live ecosystem.
- Classification ≥ 0.90, priority ≥ 0.90, routing ≥ 0.99 on the synthetic key.
- Exception recall ≥ 0.95; false-auto-action ≤ 0.02; precision ≥ 0.90.
- Duplicate suppression ≥ 0.98; missing-field/over-cost/missing-location safety = 100%.
- 100% audit completeness; dispatch idempotency on the wire; malformed rejection.
- Deterministic corpus (seed-stable fingerprint).
- Positive first-year net savings and a computed payback period under the model.

## Certification posture

The Proof Layer assigns the authoritative certification state independently. Because
the customer and data are fictional (a disclosed outcome seam), the state will not be
`CERTIFIED` or `PRODUCTION_VALIDATED`; the simulation-tier label is
`FICTIONAL_DEPLOYMENT_MODEL_CERTIFIED`.
