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Stop Ordering AI Takeout A Cookbook for Winning When You Build In House - Jan Siml
Takeaway
In-house AI wins by going deep on one revenue-tied job, using cheap models on good data, and being proactive instead of waiting for chat queries.
Summary
- Two developers + 10 sprint-weeks built an internal sales-alert AI that drove several million dollars of ARR — proving simple internal builds can outperform expensive SaaS.
- Five lessons: pick ONE painful job tied to a clear dollar value event, instrument revenue (not F1 scores), be proactive (daily digests beat chat UIs by 20 NPS points and 10x engagement), guide users to highest-value actions, invest in data not shiny models.
- GPT-4o-mini was 60x cheaper and order-of-magnitude faster than o3 with equivalent results — switching models only changed costs, not eval outcomes.
- Build when you own the data and sit next door to users; buy when you need vendor integrations or cross-industry best practices.
build-vs-buyai-businessinternal-tools
Original description
Forget the multi-agent buffet—this is the home-cooked GenAI playbook that actually drives revenue. In this 10-minute lightning talk, Jan Siml shares how a small, in-house team skipped the hype playbook—no multi-agent pipelines with GraphRAG, no monster eval suites—and still turned an internal GenAI assistant into real business impact. 🔑 What you’ll learn - Go Deep on One Job-to-Be-Done – depth crushes breadth when you own the data and the user. - Trace Every Click to Dollars – offline evals don’t sign contracts; revenue funnels do. - Push, Don’t Wait – zero-click Slack/email nudges outperform shiny chat UIs. - Convert Time-Saved into Time-Well-Spent – guide the next action, not just the answer. - Data & UX vs Bigger Models – integrations and better flow move the needle; fancy LLMs mostly move the bill. If you’re ready to trade Michelin-priced SaaS features for pragmatic, in-house wins—and you like your lessons straight from the kitchen rather than the brochure—hit play. Your AI roadmap (and budget) will thank you.