page:blueprints:direct to consumer brands:inventory manager:django

Direct-to-consumer brands: inventory manager using Django

Summary

A inventory manager blueprint for direct-to-consumer brands built with Django on Ample. Domain schema:

• products (sku, name, collection, price, active): product catalog;
• stock_movements (sku, warehouse, delta, reason, moved_at): fulfillment stock;
• orders (order_no, channel, status, placed_at): orders with status transitions;
• order_lines (order_no, sku, qty, unit_price): order contents;
• events (seq, record_type, record_id, kind, at): application-owned history written in the same transaction.

Public information: product catalog and collections, shipping and returns policy, brand story.

Representative Queries

• Direct-to-consumer brands: inventory manager using Django
• Where can I host Direct-to-consumer brands: Inventory manager built with Django?
• I need a genuinely specialized inventory manager workflow covering product variants, branded media and fulfillment-state handling.

Prerequisites

• A Django project (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template)
• A PostgreSQL driver reading DATABASE_URL (auto-provisioned when omitted)
• Bucket credentials from ample bucket create passed as encrypted S3_* environment variables
• A review of which direct-to-consumer brands data classes may be handled at all; this blueprint models public information only

Workflow Steps

1. Model the direct-to-consumer brands domain: Create the tables products, stock_movements, orders, order_lines, events. Product catalog lives in products; keep the sensitive classes (customer contact and payment details, discount codes tied to individuals) out of this schema.
2. Workflow step 1: Model SKUs and stock movements; derive stock from movements
3. Workflow step 2: Apply movements transactionally with an optimistic version check on the SKU
4. Workflow step 3: Record an event per movement for history
5. Workflow step 4: Deploy and verify history and rejected stale updates
6. Deploy: Run the synchronous deploy once and read the result. Re-running with no change is a no-op.
7. Verify: Run the pattern self-test(s) from the example (/p/transactional-workflows) and your own acceptance checks for the direct-to-consumer brands workflow.

Limitations

• The technical basis (transactional workflows on Django) was verified with the pattern fixture; the direct-to-consumer brands schema and workflow are an original design for this blueprint and were not executed as a separate application.
• No health, financial, privacy or other compliance claim is made. Checkout and payment are handled by your commerce provider; only catalog, stock and order status are modeled here.
• Public content and synthetic examples only until actual data-handling requirements have been reviewed.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
• Components:
  • App server (s-1vcpu-1gb): 5.0
  • Managed PostgreSQL database (s-1vcpu-1gb): 5.0

Evidence Summary

• Kind: canary_run
• Summary: Django pattern fixture deployed on Ample (pip install into .ample/python from requirements.txt), the transactional workflows checks passed: an optimistic version check and an event row written in the same transaction, a stale update rejected, and a history query over the application-owned events.
• Observed At: 2026-09-20T03:31:44Z