Digital FTE Productization

You've trained a specialized model. It runs reliably. It passes all quality gates. Now comes the question that separates hobbyists from professionals: How do you sell this?

A Digital FTE (Full-Time Equivalent) is more than code—it's a complete product that delivers measurable business value. This lesson transforms your Task API model from a technical achievement into a sellable product with clear value proposition, pricing, and deployment documentation.

This is the culmination of the Agent Factory thesis: building AI systems that generate revenue.

The Digital FTE Framework

┌─────────────────────────────────────────────────────────────────────────────┐
│                           DIGITAL FTE PRODUCT                                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   ┌──────────────────────────────────────────────────────────────────────┐  │
│   │                         VALUE PROPOSITION                             │  │
│   │                                                                       │  │
│   │   "TaskMaster AI replaces 8-10 hours/week of task management         │  │
│   │    overhead, delivering consistent priority decisions at             │  │
│   │    $500/month vs $4,000/month for equivalent human capacity"        │  │
│   │                                                                       │  │
│   └──────────────────────────────────────────────────────────────────────┘  │
│                                                                              │
│   ┌────────────────┐  ┌────────────────┐  ┌────────────────┐               │
│   │   CORE MODEL   │  │   DEPLOYMENT   │  │   SUPPORT      │               │
│   │                │  │   PACKAGE      │  │   PACKAGE      │               │
│   │ - GGUF export  │  │ - Docker image │  │ - Documentation │              │
│   │ - Modelfile    │  │ - Helm chart   │  │ - Integration   │              │
│   │ - Config       │  │ - Scripts      │  │ - SLA terms     │              │
│   └────────────────┘  └────────────────┘  └────────────────┘               │
│                                                                              │
│   ┌──────────────────────────────────────────────────────────────────────┐  │
│   │                         PRICING MODEL                                 │  │
│   │                                                                       │  │
│   │   License: $500/month per deployment                                 │  │
│   │   Includes: Updates, basic support, API access                       │  │
│   │   ROI: 8x compared to equivalent human task manager                  │  │
│   │                                                                       │  │
│   └──────────────────────────────────────────────────────────────────────┘  │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

The Digital FTE vs Human FTE Comparison

Understanding this comparison is critical for positioning your product:

Dimension	Human FTE	Digital FTE (Your Model)
Availability	40 hours/week	168 hours/week (24/7)
Monthly Cost	$4,000-$8,000+	$500-$2,000
Ramp-up Time	3-6 months	Instant deployment
Scalability	Linear (hire more)	Exponential (clone)
Consistency	Variable	100% consistent
Domain Expertise	Varies by hire	Encoded permanently

The key insight: Digital FTEs don't replace humans—they handle the repetitive, pattern-based work that drains human capacity. Your Task API model handles task triage, priority classification, and routine management, freeing humans for strategic decisions.

Product Package Structure

Create a complete product package:

taskmaster-fte/
├── product/
│   ├── model/
│   │   ├── taskmaster-v1.0-Q4_K_M.gguf
│   │   └── Modelfile
│   ├── deployment/
│   │   ├── docker/
│   │   │   ├── Dockerfile
│   │   │   └── docker-compose.yml
│   │   ├── kubernetes/
│   │   │   ├── deployment.yaml
│   │   │   └── service.yaml
│   │   └── scripts/
│   │       ├── install.sh
│   │       └── health-check.sh
│   └── integration/
│       ├── python/
│       │   ├── taskmaster_client.py
│       │   └── examples/
│       └── api/
│           └── openapi.yaml
├── docs/
│   ├── README.md
│   ├── QUICKSTART.md
│   ├── INTEGRATION.md
│   ├── API.md
│   └── TROUBLESHOOTING.md
├── LICENSE
├── PRICING.md
└── CHANGELOG.md

Value Proposition Documentation

Create docs/README.md:

# TaskMaster Digital FTE

> Replace 8-10 hours/week of task management overhead with intelligent automation

## What is TaskMaster?

TaskMaster is a fine-tuned AI model specialized in task management operations.
It understands your workflow vocabulary, classifies priorities accurately, and
integrates with your existing tools through standard APIs.

## Measurable Value

| Metric | Before TaskMaster | After TaskMaster | Improvement |
|--------|-------------------|------------------|-------------|
| Time spent on task triage | 10 hrs/week | 1 hr/week | 90% reduction |
| Priority classification accuracy | 72% (human average) | 94% (model) | 30% improvement |
| Response time to new tasks | 2-4 hours | < 1 minute | 240x faster |
| Task management cost | $4,000/month | $500/month | 8x reduction |

## How It Works

1. **Deploy**: Run TaskMaster on your infrastructure (Docker, Kubernetes, or bare metal)
2. **Integrate**: Connect via OpenAI-compatible API
3. **Delegate**: Route task management requests to TaskMaster
4. **Review**: Human oversight for strategic decisions only

## Capabilities

### Core Functions
- [x] Create tasks from natural language
- [x] Classify priority (low/normal/high/urgent)
- [x] Suggest due dates based on context
- [x] Update task status
- [x] List and filter tasks
- [x] Workload analysis and recommendations

### Integration Options
- [x] REST API (OpenAI-compatible)
- [x] MCP Server for AI assistants
- [x] Python SDK
- [x] CLI tool

### What TaskMaster Does NOT Do
- ❌ Make strategic business decisions
- ❌ Handle confidential HR matters
- ❌ Replace human judgment for complex situations

## Technical Specifications

| Specification | Value |
|--------------|-------|
| Model Size | 1.8 GB (Q4_K_M quantized) |
| Memory Required | 4 GB RAM minimum |
| GPU Required | Optional (CPU inference supported) |
| Latency (p50) | 200-500ms |
| Latency (p99) | < 2000ms |
| Throughput | 10-50 req/sec (hardware dependent) |
| Context Length | 4096 tokens |

## Quick Start

```bash
# Pull the Docker image
docker pull taskmaster/fte:v1.0

# Run with default settings
docker run -p 11434:11434 taskmaster/fte:v1.0

# Test the endpoint
curl http://localhost:11434/api/generate \
  -d '{"model": "taskmaster", "prompt": "Create a task for Q1 planning"}'

Support

• Documentation: https://docs.taskmaster.ai
• Email: support@taskmaster.ai
• SLA: 99.9% uptime guarantee (Enterprise tier)

## Pricing Model

Create `PRICING.md`:

```markdown
# TaskMaster Pricing

## Pricing Philosophy

TaskMaster pricing is based on value delivered, not just costs incurred.
At $500/month, TaskMaster costs 12.5% of a human task manager while
delivering 24/7 availability and consistent quality.

## Tiers

### Starter - $500/month
- 1 deployment
- 10,000 requests/month
- Community support (48hr response)
- Monthly updates

**Best for**: Small teams (5-15 people) getting started with AI task management

### Professional - $1,500/month
- Up to 5 deployments
- 100,000 requests/month
- Email support (24hr response)
- Weekly updates
- Custom prompt configuration

**Best for**: Growing teams (15-50 people) with multiple projects

### Enterprise - Custom Pricing
- Unlimited deployments
- Unlimited requests
- Dedicated support (4hr response)
- Priority updates
- Custom fine-tuning
- On-premise deployment
- SLA guarantee (99.9%)

**Best for**: Large organizations with compliance requirements

## Cost Analysis

### What You're Paying For

| Component | Cost Basis | Your Price |
|-----------|------------|------------|
| Model development | $50,000+ R&D investment | Amortized |
| Training compute | $500-2,000 per training run | Included |
| Ongoing improvements | Monthly fine-tuning | Included |
| Support infrastructure | 24/7 monitoring | Included |
| Documentation | Continuously updated | Included |

### Your ROI

**Scenario**: 20-person engineering team

| Current State | With TaskMaster |
|--------------|-----------------|
| 1 project manager spending 20 hrs/week on task management | Same PM spending 5 hrs/week |
| 15 hrs recovered per week | |
| At $75/hr loaded cost: $4,500/month saved | |
| TaskMaster cost: $500/month | |
| **Net savings: $4,000/month** | |

### ROI Timeline

Month 1: -$500 (setup and learning) Month 2: +$2,000 (partial adoption) Month 3+: +$4,000/month (full adoption)

Break-even: < 1 month Year 1 ROI: 800%

## Licensing Terms

### Permitted Uses
- Deploy on your own infrastructure
- Integrate with your applications
- Use for commercial purposes
- Create derivative applications

### Restrictions
- Do not redistribute the model files
- Do not use for competing task management products
- Do not remove attribution
- Do not use to train competing models

See LICENSE for full terms.

Deployment Documentation

Create docs/QUICKSTART.md:

# Quick Start Guide

Get TaskMaster running in 5 minutes.

## Prerequisites

- Docker (20.10+) or Kubernetes (1.24+)
- 4 GB available RAM
- 2 GB disk space

## Option 1: Docker (Recommended for getting started)

### Step 1: Pull the image

```bash
docker pull taskmaster/fte:v1.0

Step 2: Run the container

docker run -d \
  --name taskmaster \
  -p 11434:11434 \
  -v taskmaster-data:/data \
  taskmaster/fte:v1.0

Step 3: Verify it's running

curl http://localhost:11434/api/tags

Expected output:

{"models": [{"name": "taskmaster"}]}

Step 4: Make your first request

curl http://localhost:11434/api/generate \
  -H "Content-Type: application/json" \
  -d '{
    "model": "taskmaster",
    "prompt": "Create a high-priority task for quarterly review",
    "stream": false
  }'

Expected output:

{
  "response": "I'll create that task for you:\n\n**Task Created:**\n- Title: Quarterly Review\n- Priority: High\n- Status: Pending\n\nWould you like to set a due date?"
}

Option 2: Docker Compose (Recommended for production)

Step 1: Create docker-compose.yml

version: '3.8'

services:
  taskmaster:
    image: taskmaster/fte:v1.0
    ports:
      - "11434:11434"
    volumes:
      - taskmaster-data:/data
    environment:
      - OLLAMA_HOST=0.0.0.0
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
      interval: 30s
      timeout: 10s
      retries: 3

  litellm:
    image: ghcr.io/berriai/litellm:main-latest
    ports:
      - "4000:4000"
    environment:
      - OLLAMA_API_BASE=http://taskmaster:11434
    depends_on:
      - taskmaster
    restart: unless-stopped

volumes:
  taskmaster-data:

Step 2: Start the stack

docker-compose up -d

Step 3: Verify both services

# Check Ollama
curl http://localhost:11434/api/tags

# Check LiteLLM proxy
curl http://localhost:4000/health

Option 3: Kubernetes

For Kubernetes deployment, create Helm charts and manifests following the patterns from Part 7 (Chapters 50-51).

Next Steps

After deployment, consider creating:

1. Integration Guide - Document how to connect to your applications
2. API Reference - Full API documentation for your Digital FTE
3. Troubleshooting Guide - Common issues and solutions

Integration SDK

Create product/integration/python/taskmaster_client.py:

"""
TaskMaster Python SDK

Usage:
    from taskmaster_client import TaskMaster

    tm = TaskMaster(base_url="http://localhost:4000/v1")
    result = tm.create_task("Review quarterly budget", priority="high")
    print(result)
"""

from openai import OpenAI
from dataclasses import dataclass
from typing import Optional, List
import json
import re

@dataclass
class Task:
    id: str
    title: str
    priority: str
    status: str
    due_date: Optional[str] = None

class TaskMaster:
    """TaskMaster Digital FTE SDK."""

    def __init__(
        self,
        base_url: str = "http://localhost:4000/v1",
        api_key: str = "sk-local",
        model: str = "taskmaster"
    ):
        self.client = OpenAI(base_url=base_url, api_key=api_key)
        self.model = model

    def _call(self, prompt: str) -> str:
        """Make a request to TaskMaster."""
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {
                    "role": "system",
                    "content": "You are TaskMaster. Respond with structured task information."
                },
                {"role": "user", "content": prompt}
            ]
        )
        return response.choices[0].message.content

    def create_task(
        self,
        title: str,
        priority: str = "normal",
        due_date: Optional[str] = None
    ) -> dict:
        """Create a new task."""
        prompt = f"Create a task: {title}"
        if priority != "normal":
            prompt += f" with {priority} priority"
        if due_date:
            prompt += f" due {due_date}"

        response = self._call(prompt)
        return {"success": True, "response": response}

    def list_tasks(
        self,
        status: Optional[str] = None,
        priority: Optional[str] = None
    ) -> dict:
        """List tasks with optional filters."""
        prompt = "List my tasks"
        if status:
            prompt += f" that are {status}"
        if priority:
            prompt += f" with {priority} priority"

        response = self._call(prompt)
        return {"response": response}

    def analyze_workload(self) -> dict:
        """Get workload analysis and recommendations."""
        response = self._call(
            "Analyze my current workload and give me recommendations"
        )
        return {"analysis": response}

    def suggest_priority(self, task_description: str) -> dict:
        """Get priority suggestion for a task."""
        response = self._call(
            f"What priority should I assign to this task: {task_description}"
        )
        return {"suggestion": response}


# Example usage
if __name__ == "__main__":
    tm = TaskMaster()

    # Create a task
    result = tm.create_task("Review Q1 budget", priority="high")
    print("Created:", result)

    # List tasks
    tasks = tm.list_tasks(status="pending")
    print("Tasks:", tasks)

    # Get workload analysis
    analysis = tm.analyze_workload()
    print("Analysis:", analysis)

Building the Complete Package

Create scripts/build_package.py:

#!/usr/bin/env python
"""Build complete TaskMaster product package."""

import shutil
from pathlib import Path
import subprocess
import json
from datetime import datetime

def build_package(
    model_path: Path,
    version: str,
    output_dir: Path
):
    """Build complete product package."""

    package_dir = output_dir / f"taskmaster-fte-{version}"
    package_dir.mkdir(parents=True, exist_ok=True)

    print(f"Building TaskMaster FTE v{version}")

    # 1. Copy model files
    model_dir = package_dir / "product" / "model"
    model_dir.mkdir(parents=True, exist_ok=True)
    shutil.copy(model_path, model_dir / f"taskmaster-{version}.gguf")

    # 2. Generate Modelfile
    modelfile_content = f"""# TaskMaster Digital FTE v{version}
FROM ./taskmaster-{version}.gguf

PARAMETER temperature 0.7
PARAMETER num_ctx 4096

SYSTEM \"\"\"
You are TaskMaster, an intelligent task management assistant.
You help users create, manage, and organize their tasks efficiently.
Always provide clear, actionable responses.
\"\"\"
"""
    (model_dir / "Modelfile").write_text(modelfile_content)

    # 3. Copy deployment templates
    deployment_dir = package_dir / "product" / "deployment"
    shutil.copytree("templates/deployment", deployment_dir)

    # 4. Copy integration code
    integration_dir = package_dir / "product" / "integration"
    shutil.copytree("templates/integration", integration_dir)

    # 5. Copy documentation
    docs_dir = package_dir / "docs"
    shutil.copytree("templates/docs", docs_dir)

    # 6. Generate manifest
    manifest = {
        "name": "TaskMaster Digital FTE",
        "version": version,
        "build_date": datetime.now().isoformat(),
        "model_file": f"taskmaster-{version}.gguf",
        "components": [
            "model",
            "deployment/docker",
            "deployment/kubernetes",
            "integration/python",
            "docs"
        ]
    }
    (package_dir / "manifest.json").write_text(json.dumps(manifest, indent=2))

    # 7. Create archive
    archive_path = output_dir / f"taskmaster-fte-{version}.tar.gz"
    subprocess.run([
        "tar", "-czf", str(archive_path),
        "-C", str(output_dir),
        f"taskmaster-fte-{version}"
    ])

    print(f"Package built: {archive_path}")
    return archive_path

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("model_path", type=Path)
    parser.add_argument("--version", default="1.0.0")
    parser.add_argument("--output", type=Path, default=Path("./dist"))
    args = parser.parse_args()

    build_package(args.model_path, args.version, args.output)

Output:

Building TaskMaster FTE v1.0.0
Package built: ./dist/taskmaster-fte-1.0.0.tar.gz

Package contents:
  taskmaster-fte-1.0.0/
  ├── manifest.json
  ├── product/
  │   ├── model/
  │   │   ├── taskmaster-1.0.0.gguf
  │   │   └── Modelfile
  │   ├── deployment/
  │   │   ├── docker/
  │   │   └── kubernetes/
  │   └── integration/
  │       └── python/
  ├── docs/
  │   ├── README.md
  │   ├── QUICKSTART.md
  │   └── ...
  ├── LICENSE
  └── PRICING.md

Update Your Skill

Complete your LLMOps skill with:

Add a section on "Product Packaging" including:
- Digital FTE value proposition framework
- Pricing model templates
- Product package structure
- Documentation requirements
- Deployment guide templates

Try With AI

Prompt 1: Calculate Custom ROI

Help me build an ROI calculator for TaskMaster. I need:

1. Input fields:
   - Team size
   - Current hours spent on task management per week
   - Average hourly cost (loaded)
   - Expected adoption rate

2. Output:
   - Monthly savings
   - Break-even timeline
   - 12-month ROI
   - Comparison chart

Create a Python script that generates this analysis and outputs
a professional PDF report for sales conversations.

What you're learning: Building sales tools for Digital FTE products.

Prompt 2: Design Upgrade Path

I want to create a clear upgrade path from Starter to Enterprise:

1. Feature gating (what's in each tier)
2. Usage limits that encourage upgrades
3. Smooth migration between tiers
4. Enterprise negotiation framework

Help me design this upgrade path and the technical implementation
for enforcing tier limits in the deployment.

What you're learning: Designing SaaS-style monetization for AI products.

Prompt 3: Create Sales Demo

I need a compelling sales demo for TaskMaster. Design:

1. A 5-minute live demo script
2. Sample prompts that showcase key capabilities
3. Before/after comparison visuals
4. Objection handling for common concerns
5. Follow-up materials (one-pager, trial signup)

The demo should emphasize the 8x cost reduction and 24/7 availability.

What you're learning: Creating sales materials for technical products.