COMPANY SIZE: 5–500 employees, Seed–Series C, $500K–$100M ARR IDEAL ROLE: CTO, Head of AI, ML Engineer, or Founding Engineer at AI-native SaaS, developer tools, or knowledge work automation companies INDUSTRY: Developer tools, B2B SaaS, legal tech, HR tech, edtech, content platforms — any vertical integrating LLMs into core workflow TRIGGER SIGNALS: Shipping first LLM-powered feature, switching from Anthropic/Cohere due to cost or capability mismatch, raising a round with AI-native product as core thesis
POSITIONING ANGLE The pitch isn't "use GPT-4" — every engineer already knows it exists. The angle is trust and reliability at production scale. AI founders are burned by model degradation, silent API changes, and rate limit surprises in prod. Lead with stability, SLAs, and the platform ecosystem (fine-tuning, embeddings, Assistants API) as a unified substrate vs. stitching together providers. SUBJECT LINE Your LLM stack decision is bigger than the model benchmark OPENING (first 2 sentences) Noticed you're shipping your first LLM feature — the benchmark you're optimizing for in dev is almost never the bottleneck that bites you in production. Teams at your stage usually hit rate limits, context window edge cases, or silent model behavior shifts within the first 30 days of real traffic.
1. GITHUB: New dependencies on openai, anthropic-sdk, or langchain added to a previously non-AI repo → LLM integration just started 2. JOB BOARDS: "AI Engineer" or "LLM Engineer" first hire → company is going AI-native for the first time 3. TWITTER/X: Founder tweets about model comparison, GPT-4 vs Claude results, or "we switched to X" → active vendor evaluation 4. LINKEDIN: "We just shipped AI-powered [feature]" announcement → product is live, infrastructure decision locked or re-evaluatable 5. CRUNCHBASE: AI-native company raises Seed or Series A → new capital, first time building for production scale