Ajaia Delivery Ops

Patient Acquisition & Growth Agent — Delivery Ops

**Ajaia AI Consultancy | Take-Home Assignment** **Delivery Lead:** Manogna Vanam

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What This Is

A delivery operations system for the **Patient Acquisition & Growth Agent** engagement — a hospital client implementing an AI agent that engages inbound and dormant patient leads and books appointments directly into provider calendars.

Built using the **DOE framework (Directives → Orchestration → Execution)**:

• directives/ — SOPs per workstream (living documents, updated as the engagement progresses)
• execution/ — Deterministic Python scripts (the prototype and status generator)
• docs/ — Human-readable submission deliverables

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Quickstart

# 1. Clone / download the project
cd Ajaia

# 2. Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate        # Mac/Linux
# venv\Scripts\activate         # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Set your API key (optional — mock fallback works without it)
cp .env.example .env
# Edit .env and add: ANTHROPIC_API_KEY=your-key-here

# 5. Launch the delivery dashboard
streamlit run execution/launch_readiness_tracker.py

# 6. Generate a weekly status update
python execution/generate_status_update.py

Dashboard opens at **http://localhost:8501**

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Project Structure

Ajaia/
│
├── CLAUDE.md / AGENTS.md / GEMINI.md   # DOE framework instructions (AI-readable)
├── README.md                            # This file
├── requirements.txt                     # Python dependencies
├── .env.example                         # Secrets template (safe to share)
├── .env                                 # Real secrets (gitignored — never commit)
│
├── directives/                          # Layer 1: SOPs (what to do)
│   ├── ws1_discovery_requirements.md
│   ├── ws2_compliance_privacy.md
│   ├── ws3_integrations_scheduling.md
│   ├── ws4_conversation_design.md
│   ├── ws5_qa_pilot_readiness.md
│