ASTRID

I’m building five products in parallel. HealthOS, Startup-VC-People Knowledge graph and three more. CLAUDE Code gave us speed. But somewhere between the third silent production failure and the morning ten students couldn’t log in at class time — I realized (again) speed without knowledge & discipline has a tax. So I started encoding that instead. Aura is a quality gate built on top of Claude Code. She runs before anything ships. But what makes her different from a standard AI coding assistant isn’t what she checks — it’s what she knows, remembers, and can do on her own. She reviews architecture and recommends tools. Not from memory — she searches the web, reads current documentation, compares alternatives against what’s actually maintained today, and flags decisions that will create problems six months from now. She formally records those decisions so they don’t get re-litigated every few weeks. Opinionated, and she cites her sources. She learns from incidents. When the same failure class shows up three times, she writes a permanent rule and checks every future push against it. We had a 3-hour outage from a data worker with a missing time-window guard. She now catches that pattern in every codebase, every push, without being reminded. She speaks infrastructure from “Hetzner” to Docker Compose, deployment and zero trust networks. She knows how they break, not just how they’re configured. She runs multiple nodes, distributes work across them when it makes sense, and knows when to scale out versus when adding nodes just multiplies the problem. She runs mostly on local hardware. ~ 50 Mio tokens/day — architecture reviews, pipeline decisions, code analysis, data enrichment — processed on our own servers, not sent to a cloud API. Cloud costs stay minimal. Data stays in-house. Maybe that’s the difference between AI as an experiment and AI as an actual system. She understands production data pipelines, that’s a part most people genuinely underestimate (Ilya Vladimirskiy 😉). No AI handles garbage input well. A pipeline that trusts bad data writes bad data, and bad data propagates through everything downstream. She knows circuit breakers, source validation, concurrency limits, time-window guards on workers. What she does before every push: security and dependency scan, database migration safety check, observability verification, API performance regression, cost impact when AI prompts change. After every deploy: confirms the new version is actually live. Between tasks: picks the highest-severity open backlog item and works it.

What she can’t replace: the organizational side of a CTO. Hiring, culture, the political capital that comes from years of production war stories. But on the technical side, she’s a genuine partner.

For investors evaluating early-stage technical teams: the discipline is in the process, not just in whoever happened to be careful that day.

Happy to share more with VCs and Founders on how it’s built.