# Management consultancies: project tracker using Django

## Summary
A project tracker 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;  
- **state_transitions** (record_type, from_state, to_state): the allowed state machine. 
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 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).

## Representative Queries
- Management consultancies: project tracker using Django  
- Where can I host Management consultancies: Project tracker built with Django?  
- I need a genuinely specialized project tracker workflow covering engagement milestones, deliverable versions and client approvals.

## Resource Requirements
- **primitive:compute**  
- **primitive:postgres**  
- **primitive:s3-compatible-object-storage**

## Infrastructure Requirements
- **Compute:** Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.  
- **Postgres:** 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:** Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

## Pre-requisites
- 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

## Workflow Steps
1. **Model the management consultancies domain:** Create the tables client_entities, engagements, workstreams, deliverables, state_transitions. 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 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.
7. **Verify:** Run the pattern self-test(s) from the example (/p/relational-records) and your own acceptance checks for the management consultancies workflow.

## Examples
- **Django pattern fixture:** Verified relational records basis for this blueprint.

## Success Checks
- App responds on its public URL  
- Relational-records self-test from the example.

## Limitations
- The technical basis (relational records 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.

## Cost Estimate
- **Currency:** USD  
- **Monthly Amount:** 10.0  
- **Components:**  
  - App server (size: s-1vcpu-1gb): $5.0  
  - Managed PostgreSQL database (size: s-1vcpu-1gb): $5.0