I’m Paul Philp — a greybeard software engineer and AI systems architect. I’ve spent decades building and scaling software, and in the last few years I’ve gone deep on modern TypeScript, Effect, Next.js, and AI tooling.

Today I help product teams ship sound AI systems: real features built on LLMs and modern web stacks that you can observe, debug, and trust in production.

From leadership back to building

Earlier in my career I moved from hands-on engineering into leadership roles — tech lead, executive, and eventually co-founding CEO of a B2B SaaS startup. After that company failed, I made a deliberate choice: step away from the executive track and return to what I’ve always loved most: building systems.

I retooled from the ground up: modern TypeScript, React/Next.js, Tailwind, Effect-TS, LangGraph, Vercel AI SDK, and production-grade AI patterns. I shipped small projects, CLIs, and internal tools to rebuild the habits that matter: clear invariants, strong typing, tests, observability, and honest post-mortems.

What that experience taught me

• Systems thinking. Whether it’s a codebase, a team, or a business, failures are almost never about a single line of code. They’re about the interactions and feedback loops.
• Humility in front of the code. Specs, decks, and roadmaps are useful, but the running system is the final truth.
• Clarity over heroics. Sound systems come from clear contracts, well-understood failure modes, and steady delivery — not 2 a.m. saves.
• Good boundaries make good systems. The same clarity that keeps human relationships healthy makes software easier to reason about: explicit interfaces, explicit ownership, explicit trade-offs.

How I work with teams

I usually join as a senior IC or staff-level partner to your existing tech lead or CTO. My job is to:

• Design or refine the architecture for AI-powered features in your product.
• Take fragile prototypes and harden them for production with proper logging, tracing, evals, and guardrails.
• Raise the bar for the whole team’s engineering practice — especially around TypeScript, Effect, and AI-assisted workflows.
• Leave behind patterns, documentation, and observability so you can keep moving fast after I’m gone.

A note on failure and rebuilding

I’ve been through a startup failure and a personal reset. It forced me to examine not just how systems fail, but how people and teams fail together. That history shapes how I show up now: calm under pressure, direct about risk, and serious about building teams and systems that don’t quietly drift into bad states.

If you’re curious about the personal side and the parallels with complex systems and AI, there are essays on the blog. If you’re here because you need reliable AI features in production, we can start there and stay there.

Where to go next

• Homepage: overview of how I help teams ship sound AI systems.
• Blog: deep dives on Effect-TS, AI architecture, and building reliable LLM-powered features.
• Connect: if you’re shipping AI features and need them to behave in the real world, I’d be happy to hear what you’re building.