django.md

Architecture practices: project tracker using Django

Summary

A project tracker blueprint for architecture practices built with Django on Ample. Domain schema: client_entities (name, entity_type, site_address): clients and project sites; projects (client_entity_id, project_name, stage, period_start, period_end): engagement periods by RIBA/AIA-style stage; drawing_sets (project_id, set_name, revision, object_key, issued_at): versioned drawing issues; approvals (project_id, drawing_set_id, approver_role, decided_at, decision): client sign-offs per issue; state_transitions (record_type, from_state, to_state): the allowed state machine. Public information: portfolio and services, team and accreditations, studio locations. Kept out of scope until handling is reviewed: unreleased drawings and site surveys, planning submissions before filing, client contact details. Drawings and surveys are client-confidential until issued; the portal models per-project authorization only. Technical basis verified on Django: a workspace-scoped table, an explicit state machine (draft to review to published) that rejects invalid transitions, and a cross-workspace read that returns nothing (transitions=true isolation=true).

Infrastructure requirements

Prerequisites

Exact tested configuration

Steps

  1. Model the architecture practices domain. Create the tables client_entities, projects, drawing_sets, approvals, state_transitions. Clients and project sites live in client_entities; keep the sensitive classes (unreleased drawings and site surveys, planning submissions before filing, client contact details) out of this schema.
  2. Workflow step 1. Model engagements and workstreams with explicit status transitions.
  3. Workflow step 2. Scope every query by client entity.
  4. Workflow step 3. Attach deliverables as private objects linked to workstreams.
  5. Workflow step 4. Deploy and verify transitions and cross-client isolation.
  6. Deploy. Run the synchronous deploy once and read the result. Re-running with no change is a no-op.
   ample deploy . --name <app-name> --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
  1. Verify. Run the pattern self-test(s) from the example (/p/relational-records) and your own acceptance checks for the architecture practices workflow.
   ample logs <deployment_id> --kind build

Tested examples

Success checks

Limitations

Cost estimate

Estimated 10.00 USD per month (size prices from pricing.toml at build revision a1b8c38919e59cd035ebabaced73cf84ece24371).

Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.

Verification evidence

Last verified: 2026-09-20T03:31:44Z

Execution binding

MCP tool ample_deploy (registry mcp:ample_deploy), schema hash 876465fce906da0c observed 2026-09-21T03:19:59.461917+00:00 at revision 49962bcade4f, binding state current, required scopes: servers:write, databases:read, buckets:read.