September 12, 2025
Read Time: ~5 minutes
<aside> đź“Ś At Tilt, we seek individuals who challenge personal assumptions, value ownership and trust, and strive for excellence to inspire and empower their team. If this article connected with you, join our team!
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<aside> đź“Ž Most companies think buying an AI tool is the hard part, but the real challenge is building the scaffolding that makes it useful. At Tilt, adoption only worked once engineers invested in documentation, rules, and workflows that embedded AI into daily practice. This post explores how that infrastructure was built, why it matters, and what other teams can learn from it.
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https://open.spotify.com/episode/0qUi1Ba1WudZPcaTLxsn2i?si=4c7dfb0e5d0047bd
Every day on Reddit, another engineer vents about leadership's latest AI mandate. The frustration is understandable. At Tilt, we're unapologetic about our AI adoption push, but here's the thing: we learned early that buying the tool is just the beginning.
When I dig into these frustrated posts, the pattern is clear. Companies invest in the AI platform itself, then wonder why adoption stalls. What's missing? The infrastructure that makes AI actually useful—training, documentation, and systematic integration into existing workflows.
This post breaks down how we built that infrastructure at Tilt. I'm going to call out specific developers who drove this work for two reasons: first, to show that successful AI adoption requires company-wide effort, not just a budget line item. Second, because our candidates read these posts, and I want them to see what their potential teammates are passionate about building.
Notion Documents (Core Concepts)
These are our human-readable philosophy docs—living documents where we hash out "the Tilt way." Several engineers led this initiative to codify our engineering principles across areas like database design, telemetry standards, and third-party library evaluation criteria.
These documents stay in Notion because we want the comment history, the debate, the evolution of thinking. New hires can see not just what we decided, but why. While agents rarely reference these directly during coding, they're accessible through our Notion MCP server when deeper context is needed. They were also used for the initial attempt at our Augment Rules.
Augment Rules (The Heavy Lifters)
This is where the Agentic magic happens. Augment rules translate our human concepts into AI-digestible guidance. They're verbose, explicit, and frankly boring to read—which is exactly what makes them effective.
We organize these into three categories: