Founder-level use cases for pre-seed through Series A. Research, outreach, content drafting, fundraising prep. What actually moves the needle when you're 1–10 people.
Use SaaS for everything. Notion AI, ChatGPT Plus, Claude Pro. Don't build custom yet — you don't know what your workflow even is. Spend $100/mo on tools, not $10k on a custom agent.
First custom agent. Pick the one workflow that's eating 20+ hours/month. Usually customer research synthesis, founder content engine, or outreach research. Build the agent once, save hours forever.
Fundraising-stack agents. Build the investor research + diligence pre-drafter 6 months before you start fundraising. By the time you're in active investor conversations, the workflow is automatic.
Operator-grade agent systems. Move from single agents to multi-loop systems. Customer success agent + content engine + ops analytics, all integrated. This is what I build via the agent build engagement.
Three things that look attractive but waste startup time + capital:
In-house when the agent IS the product (you're an AI-native company). SaaS for everything else — onboarding tools, research agents, content drafting. Most pre-Series A startups should use SaaS aggressively and only custom-build when SaaS leaves a competitive moat unbuilt.
In 2026, investors expect you to be using AI — both internally (operational efficiency) and in-product if it makes sense. The story shifted: "we don't use AI" is now a flag. "We use AI throughout our ops" is table stakes. "Our agent system is a moat" requires specific defensibility.
Different conversation. Product AI requires careful evaluation, evals + guardrails, prompt engineering specific to your domain, and ongoing investment. If your product needs AI to work, build it in-house with senior engineering attention. If AI just makes it nicer, integrate via API.
Three moats that work: 1) Proprietary data (training data, customer behavior data, integrations). 2) Workflow integration (you're embedded in their daily work). 3) Distribution. None of those are about the model itself. The wrapper criticism is about lack of moat, not about using AI.
Three agents I'd build pre-Series A: 1) Investor research agent (pulls portfolio companies, recent investments, partner backgrounds from their site + LinkedIn + Twitter). 2) Pitch-deck QA agent (Claude reading your deck against common investor pushback). 3) Diligence answer drafter (pre-drafts answers to common DD questions for your data room).
Free 30-min intro. I'll tell you honestly what's worth building vs. buying for your stage.