100,000 Work Order Simulation
A FICTIONAL / SYNTHETIC enterprise deployment model: the Work Order Agent Ecosystem run against 100,000 synthetic property-management work orders, with a transparent ROI. No real customer data.
Headline metrics
From the full 100,000-order synthetic run over the real ecosystem (real PostgreSQL + real gRPC). Illustrative — not a real deployment.
*Illustrative ROI from stated assumptions in assumptions.json; the only measured input is the auto-action rate. Full math in the impact study.
Before & after
The fictional manual process vs. the agent-assisted model.
Before — 100% manual
- Every one of 100,000 orders read by a human
- ~8 minutes handling per order
- 14-person coordination team
- Slow routing · duplicate tickets · inconsistent vendor assignment
- SLA misses · weak auditability
After — exceptions only
- Agents classify → route → validate → action every order
- 56.6% auto-dispatched with no human touch
- Humans handle only the 43.3% flagged as exceptions
- Duplicates suppressed · emergencies escalated · missing info bounced
- 100% append-only audit trail · idempotent dispatch
Automation funnel
Where 100,000 synthetic work orders ended up.
Financial impact (illustrative)
Manual baseline vs. agent-assisted, from stated assumptions. Every line shows its arithmetic in evidence/roi.json.
| Line | Value |
|---|---|
| Manual baseline labor | 13,333.33 h → $506,667/yr |
| Agent-assisted exception labor | 3,612.5 h → $137,275/yr |
| Annual labor savings | $369,392 |
| Implementation (one-time) | $185,000 |
| Platform (annual) | $114,000 |
| First-year net savings | $70,392 |
| Payback period | 8.69 months |
| 3-year net savings / ROI | $581,175 · 110.28% |
Risk controls
Conservative validator
Low-confidence, over-cost, duplicate, and missing-field orders are never auto-actioned. False-auto-action rate: 0.00%.
Human-in-the-loop
43.3% of volume kept for human judgment by design; exception recall 100.0%.
Idempotent dispatch
Retries never double-dispatch (verified on the gRPC wire); malformed payloads rejected by the server.
Full auditability
100.0% of orders carry an append-only audit row; records read back from the live DB.
Deterministic
Seed 20260625 → identical corpus + identical metrics; a fingerprint check enforces it.
Honest certification
Proof Layer state PROOF_INCOMPLETE (not PRODUCTION_VALIDATED — fictional data). Simulation label FICTIONAL_DEPLOYMENT_MODEL_CERTIFIED.
Proof artifacts & documents
Every claim traces to evidence. The Proof Layer (IRS_AUDITOR) has final authority over the certification state.
Impact Study
The full PDF-ready executive report
Overview
What it is and how to run it
Architecture
Pipeline + module map
Outcome Contract
Scope, seams, success criteria
Dataset Card
Synthetic data schema + method
Verification Report
All MUST_PASS checks + benchmark
Certification Report
Posture + why not PRODUCTION_VALIDATED
Executive Evidence
The 10 IRS_AUDITOR questions
Limitations
Every disclosed seam
Auditor Objections
Hostile objections answered
Auditor Challenge
Generated interrogation
Evidence Grade
Grade + basis
Reproduce
Exact commands
Verify
What each check asserts
Social Package
Clearly-labeled marketing copy
User Guide
Run the simulation + reports
Run & Deploy
Local run + real-deployment seams
Release Notes
v1.0.0 summary
Honest scope
This is a fictional enterprise simulation / synthetic deployment model. The customer, the 42,000-unit portfolio, the 100,000 work orders, and all cost assumptions are invented. The agent ecosystem, the PostgreSQL persistence, and the gRPC dispatch are real and run unmodified; only the inbound volume is synthetic. The ROI is a transparent model, not a realized result. The Proof Layer assigns the authoritative certification state — intentionally not PRODUCTION_VALIDATED, because no real customer data exists. See the limitations and certification report.
Delivery metrics
Tokens, elapsed time, and cost for producing this outcome. Basis: Estimated — reproducible model over this outcome’s published artifacts. metrics.json
Cost and tokens are estimates derived deterministically from published artifacts and representative list pricing; actual billing may differ. Model basis: claude-sonnet-class (representative).