About

A coworker, not another tool.

We built Crewly because we kept watching our friends — founders, ops people, sharp builders — open a new tab to ChatGPT, paste in something from Slack, ask a question, copy the answer back. Friction stacked on friction. We thought: the coworker should be in the room.

Why now

The agent layer finally works. Anthropic, Google, and OpenAI ship models that can hold a conversation, use tools, and remember what they were told. The open-source community ships templates for every common skill. The Model Context Protocol means we can connect to GitHub, Notion, Postgres, anything — without writing one-off integrations.

Two years ago, this was a research demo. Six months ago, this was a "could work, but the latency." Today, it's a coworker. So we built one.

What we believe

A good tool fades into the work. A great one anticipates it.

Crewly's design is shaped by three convictions. First, that the right place for AI is where the work already happens — not a separate destination. Second, that memory is the difference between a chatbot and a colleague. Third, that the best products feel handmade, even when the engine behind them is industrial.

"The coworker should be in the room — with the context the room has."

Built in Atlanta

I'm a solo founder and AI engineer based in Atlanta. I build Crewly the way a forward-deployed engineer does — embedded close to the problem, shipping fast, and hardening what real teams hit in production. Crewly launched in May 2026 and is onboarding its first design-partner teams now.

Open by default

Crewly is built on top of the Apache-2.0 awesome-llm-apps library. We contribute back. Our skill manifests are open. Our memory format is documented. If we ever shut down, your data exports as portable Markdown + JSON, with no lock-in.