Jack81st / launchguard-ai

# LaunchGuard AI
**A reviewable product-launch workflow for pricing, policy checks, and Shopify drafts.**
Quickstart · Architecture · Engineering Report · Threat Model · Roadmap · Contributing
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## The problem
A supplier record can become a product listing before anyone has checked the margin assumptions, applicable policies, or approval history. LaunchGuard keeps those decisions in one traceable workflow:
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LaunchGuard works from catalog and cost data supplied by the operator. It does not estimate demand or marketplace sales. If required evidence or approval is missing, the run stops before Shopify delivery.
## How it works
| Design choice | Implementation | Operational value |
|---|---|---|
| Checkpointed review | LangGraph `interrupt()` plus SQLite checkpoints and stable thread IDs | A run can pause, survive process restart, and resume after review |
| Deterministic business rules | FX, pricing, retrieval, compliance, persistence, and delivery use explicit services | Calculations and controls remain inspectable and testable |
| Traceable inputs | Strict CSV/JSON SKU contract with a source reference | Reviewers can see where the commercial assumptions came from |
| Versioned policy retrieval | SQLite FTS5, market filters, effective dates, source paths, and citation IDs | Reviewers can inspect exactly which rule supported the draft |
| Unit economics | Duty, shipping, fulfillment, fees, VAT, returns, ads, margin, FX, and four stress scenarios | Makes commercial feasibility visible before copy is approved |
| Draft-only delivery | Dry-run default, explicit review, deterministic handle, remote lookup, local ledger, Shopify `DRAFT` status | Reduces accidental publishing and duplicate products |
| Evaluation gate | Reproducible cases for clean launches, commercial warnings, and prompt-injection blocking | Changes can be measured instead of judged only by demos |
## Control room
Use the control room to start a launch review, compare the recommended price with its floor, inspect the FX source and policy citations, and approve or reject the draft. Runs waiting for review remain available after a restart.
## Quickstart
### Local Python
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Open http://127.0.0.1:8080. The default setup works without model or Shopify credentials.
### Docker
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SQLite run history and checkpoints are stored in the `launchguard-data` volume.
## Durable CLI review
Start an offline demonstration run:
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The command returns a `run_id` and exits with `pending_approval`. The process can now stop. Resume the same checkpoint later:
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Reusing a completed `run_id` does not create another delivery.
## Pricing model
LaunchGuard solves the price required to preserve the target contribution margin:
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Each review includes:
- base economics;
- 3% adverse FX movement;
- +2 percentage points in returns;
- +1 percentage point in platform fees.
The pricing result is decision support, not a forecast. Every input appears in the review package so the assump