Define the Problem (Market-First, Not Tech-First)

• Identify a high-frequency, high-cost pain point
• Validate it with real users or operators
• Confirm the problem generates or relies on large data streams
• Define what 10x better looks like with AI

Design an AI-First Architecture

• Choose where AI delivers core value (e.g. output, decision, automation)
• Map out the data flow (input → model → feedback → retrain)
• Determine model approach: LLM, CV, time series, hybrid
• Select AI infra: vector DB, LLM provider, data labeling, etc.

Assemble Your Founding Team

• ML/AI engineer with deployment experience
• Domain expert (deep industry knowledge)
• Product/UX builder to turn models into experiences
• Optional: operations or GTM expert for early growth

Secure or Generate Proprietary Data

• Identify or negotiate access to unique datasets
• Build a system to collect structured feedback from users
• Plan for data labeling (manual, synthetic, weak supervision)
• Ensure data privacy & compliance from day one

Build the First AI-Powered MVP

• Define narrow, specific use case for MVP
• Ship a functional prototype using real user data
• Include human-in-the-loop where model is uncertain
• Track usage, feedback, and accuracy from launch

Measure Value, Not Just Accuracy

• Define clear ROI metrics (e.g. time saved, errors avoided)
• Track before/after benchmarks with early users
• Use data to prove the AI is the reason for the impact
• Set goals for model improvement and value per user

Build Scalable AI-First Economics

• Keep marginal cost low as volume scales
• Automate wherever the model has >90% confidence
• Structure pricing around value delivered (not usage)
• Plan for gross margins >70% with automation at scale

Create Feedback & Training Loops

• Log every input/output/user correction
• Retrain models with updated real-world usage
• Track performance deltas across versions
• Use fine-tuning or LoRA as model improves

Prepare for Distribution

• Define who owns the problem internally (buyer persona)
• Build a demo that shows clear before/after
• Create bottom-up growth path (self-serve or pilot)
• Position as "AI-native solution," not just AI add-on

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