Implementation Guide

Your 90-Day Roadmap to Launch Unified Machines as a Software-First AI Venture

For Non-Technical Founders: Start Smart, Not Hard

You don't need hardware or a PhD to build in AGI. The most successful AI startups in 2025 leverage existing foundation models, cloud infrastructure, and open-source frameworks. Your competitive advantage comes from proprietary data, vertical expertise, and innovative application of causal reasoning—not from building infrastructure from scratch.

Three Strategic Approaches

Recommended

Fine-Tuning + Causal Layer

Start with pre-trained foundation models (Llama 3, Mistral) and fine-tune on proprietary data. Add custom causal reasoning layer for differentiation.

Advantages

  • Fast time-to-market: 3-6 months
  • Low capital: $200K-500K Year 1
  • No hardware needed (rent GPUs)
  • Clear differentiation through causal reasoning
  • Proven path to product-market fit

Challenges

  • Dependent on third-party models
  • Need proprietary data for moat
Alternative

API Layer + Agent Orchestration

Use commercial APIs (OpenAI, Anthropic) and focus on building sophisticated agent orchestration and workflow automation.

Advantages

  • Fastest launch: 1-2 months
  • Minimal technical complexity
  • Lowest initial capital: $50K-150K
  • Access to best-in-class models

Challenges

  • Weak differentiation (commoditized)
  • High ongoing API costs
  • Limited control over model behavior
Not Recommended

Build from Scratch

Train custom foundation models from scratch with proprietary architecture and training data.

Advantages

  • Maximum differentiation
  • Full control over architecture
  • Strong IP and defensibility

Challenges

  • Requires $5M-10M+ capital
  • 12-18 month development cycle
  • Need world-class research team
  • High technical risk

90-Day Launch Roadmap

Phase 1: Foundation (Weeks 1-4)

Month 1: Validate & Setup

Week 1: Market Validation
  • Choose target vertical (manufacturing/logistics/healthcare)
  • Conduct 20 customer discovery interviews
  • Identify top 3 pain points
  • Validate willingness to pay
Week 2: Technical Setup
  • Create AWS account + apply for startup credits
  • Setup development environment
  • Choose base model (Llama 3 recommended)
  • Setup Lambda Labs GPU account
Week 3: Data Collection
  • Identify data sources for vertical
  • Setup data collection pipeline
  • Create initial training dataset (1K-10K examples)
  • Build causal graph for domain
Week 4: Team Building
  • Write job descriptions for ML engineer
  • Post on relevant platforms
  • Begin technical interviews
  • Identify advisor/consultant for interim support

Phase 2: Build (Weeks 5-8)

Month 2: MVP Development

Week 5: Model Fine-Tuning
  • Setup training pipeline with LoRA
  • Run first fine-tuning experiment
  • Evaluate model performance
  • Iterate on hyperparameters
Week 6: Causal Integration
  • Implement causal inference layer
  • Connect causal graph to model outputs
  • Test counterfactual reasoning
  • Validate intervention predictions
Week 7: Agent Development
  • Build agent orchestration with LangChain
  • Implement tool use and API integrations
  • Create user interface (web app)
  • Setup vector database for RAG
Week 8: Testing & Refinement
  • Internal testing with real scenarios
  • Fix bugs and edge cases
  • Optimize performance and latency
  • Prepare demo materials

Phase 3: Launch (Weeks 9-12)

Month 3: Pilot Deployment

Week 9: Design Partner Outreach
  • Reach out to 10 potential design partners
  • Schedule product demos
  • Negotiate pilot terms
  • Secure 2-3 pilot commitments
Week 10: Pilot Deployment
  • Deploy to pilot customers
  • Setup monitoring and logging
  • Collect usage data and feedback
  • Provide customer support
Week 11: Iteration
  • Analyze pilot results
  • Implement customer feedback
  • Improve model accuracy
  • Refine user experience
Week 12: Fundraising Prep
  • Document pilot results and metrics
  • Create pitch deck with traction
  • Identify target investors
  • Begin fundraising conversations

Budget Breakdown

Year 1 Monthly Operating Budget

Category Monthly Cost Annual Cost Notes
Cloud Infrastructure $3,000 - $5,000 $36K - $60K AWS Bedrock, SageMaker, S3 storage
GPU Compute $2,000 - $5,000 $24K - $60K Lambda Labs, RunPod for training runs
Model APIs $1,000 - $3,000 $12K - $36K OpenAI, Anthropic for benchmarking
Development Tools $500 - $1,000 $6K - $12K GitHub, monitoring, analytics
Data & Datasets $1,000 - $2,000 $12K - $24K Proprietary data collection, labeling
Salaries (1-2 engineers) $12,000 - $25,000 $144K - $300K ML engineer + robotics engineer
Operations & Legal $2,000 - $4,000 $24K - $48K Incorporation, insurance, accounting
TOTAL $21,500 - $45,000 $258K - $540K Year 1 Operating Budget

Capital Efficiency Strategy

Start lean with $200K-300K seed capital. Use AWS startup credits ($100K available), negotiate deferred compensation with early hires (equity-heavy packages), and leverage open-source tools aggressively. Aim for pilot revenue within 6 months to extend runway and validate product-market fit before raising Series A.

First 3 Hires

Hire #1

Senior ML Engineer

Salary: $150K-200K base + 1-2% equity
Timeline: Month 1-2

Key Responsibilities

  • Fine-tune foundation models
  • Build training pipelines
  • Implement causal reasoning layer
  • Optimize model performance
Hire #2

Robotics/Embodied AI Engineer

Salary: $140K-180K base + 0.5-1% equity
Timeline: Month 3-4

Key Responsibilities

  • Integrate models with robot platforms
  • Build simulation environments
  • Develop perception and control systems
  • Deploy to physical hardware
Hire #3

Full-Stack Engineer

Salary: $120K-160K base + 0.5-1% equity
Timeline: Month 4-5

Key Responsibilities

  • Build user-facing applications
  • Create APIs and integrations
  • Implement monitoring and logging
  • Handle deployment and DevOps

Ready to Execute?

Dive into our AGI framework to understand the Causal World Models approach, or explore the functional prototype to see the concept in action.

Explore AGI Framework View Prototype Demo