Management consultancies: client portal using Django

Summary

A client portal blueprint for management consultancies built with Django on Ample. Domain schema: client_entities (name, industry, primary_contact_role): client organizations; engagements (client_entity_id, scope_title, phase, period_start, period_end): engagement periods and phases; workstreams (engagement_id, name, owner, status, due_at): tracked workstreams; deliverables (engagement_id, title, version, object_key, released_at): versioned deliverables shared with the client; document_access (document_id, principal_reference, role, granted_at): who may read which document. Public information: service offerings and case studies (anonymized), consultant profiles, engagement model. Kept out of scope until handling is reviewed: client strategy documents, financial models, interview notes. Client strategy material is confidential; authorization is modeled per engagement and no compliance claim is made. Technical basis verified on Django: a role-to-document access model with negative tests (401 without identity, 403 for the wrong role) and a private-bucket round-trip for the allowed role (private=ok).

Infrastructure requirements

• Compute: verified (Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.)
• Postgres: verified (Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.)
• S3-compatible object storage: verified (Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.)

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 management consultancies data classes may be handled at all; this blueprint models public information only

Exact tested configuration

• template: python-3.12
• runtime: python
• size: s-1vcpu-1gb
• install: python3 -m pip install --target .ample/python -r requirements.txt
• start: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Steps

1. Model the management consultancies domain. Create the tables client_entities, engagements, workstreams, deliverables, document_access. Client organizations live in client_entities; keep the sensitive classes (client strategy documents, financial models, interview notes) out of this schema.
2. Workflow step 1. Model client entities and engagement periods; every document belongs to one engagement
3. Workflow step 2. Authorize by client entity and role before any storage access (401 without identity, 403 for the wrong client)
4. Workflow step 3. Store documents in the private bucket and stream them through the app
5. Workflow step 4. Deploy and verify the negative authorization tests and an authorized download
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=...

7. Verify. Run the pattern self-test(s) from the example (/p/private-document-library) and your own acceptance checks for the management consultancies workflow.

   ample logs <deployment_id> --kind build

Tested examples

• Django pattern fixture (tests/deploy-canaries/django-patterns): Verified private document library basis for this blueprint.

Success checks

• app responds on its public URL (/ on the live URL, expect ample canary django patterns)
• private-document-library self-test from the example (/p/private-document-library on the live URL, expect see the pattern fixture checks)

Limitations

• The technical basis (private document library on Django) was verified with the pattern fixture; the management consultancies 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. Client strategy material is confidential; authorization is modeled per engagement and no compliance claim is made.
• Public content and synthetic examples only until actual data-handling requirements have been reviewed.
• Verified on the python-3.12 template at s-1vcpu-1gb; region, request-duration limits and other sizes are unknown or unverified.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified.
• PutObject and GetObject with path-style addressing are verified; other S3 operations and CDN cache rules are not.

Cost estimate

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

• app server x1 s-1vcpu-1gb: 5.00 USD
• managed PostgreSQL database x1 s-1vcpu-1gb: 5.00 USD

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

• canary_run on 2026-09-20T03:31:44Z at revision 03b6402b1b3e19178985f08ea4033804521d85d4-dirty (CLI b75e104): Django pattern fixture deployed on Ample (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template); the private document library checks passed: a role-to-document access model with negative tests (401 without identity, 403 for the wrong role) and a private-bucket round-trip for the allowed role (private=ok). The blueprint's Management consultancies schema and workflow below build on that verified basis and were not separately executed.