Built to run reliably in real production environments.
Production SaaS architect with hands-on experience designing, deploying, and operating AI agents in live business environments. Builds the systems that stay up.
Operations and execution specialist focused on scaling AI-driven workflows from prototype to production. Turns technical capability into business outcomes.
Designed, deployed, and operated AI agents in live production environments.
Most AI agent companies optimize for impressive demos. We optimize for uptime.
90% of AI agent startups build for demos and fundraising. Their systems crash under real load, leak context between sessions, and fail silently when APIs change.
We build crash recovery, session isolation, cascading failure prevention, and graceful degradation into the core. Not bolted on — built in from day one.
Anyone can wrap an LLM in an API. The hard part is making it work at 3 AM on a Saturday when the upstream provider changes their response format.
"The hard part of agents isn't reasoning — it's making them dependable."
Contributions to OpenClaw — the open-source agentic AI platform now moving under OpenAI.
Identified and patched production failure modes in the OpenClaw gateway that affected session stability under concurrent agent load. Fixes accepted into the main codebase.
Contributed crash recovery and automatic restart logic for long-running agent sessions. Prevents the silent failures that plague most agentic deployments.
Designed and implemented circuit-breaker patterns that stop a single agent failure from taking down the entire gateway. Battle-tested in multi-agent production environments.
Built model cooldown tracking, provider fallback chains, and token rotation automation. The operational infrastructure that makes agents viable for business use.
Contributed to the ecosystem while keeping proprietary architecture private.
Why production-grade operations creates an unassailable position.
Every month in production generates operational knowledge that can't be replicated by reading docs or running benchmarks. Real failure modes, real recovery patterns, real edge cases.
Our systems have survived provider outages, API changes, token expirations, and cascading failures. Each incident makes the platform more resilient. Competitors start from zero.
Enterprises are moving from AI experiments to production deployments. They're discovering that demos don't scale. The demand for reliability is about to explode.
We become the infrastructure layer that makes AI agents production-viable. Every company deploying agents needs what we've already built and battle-tested.
Strategic connections before capital. We want to find the niche, then scale.
We're looking for mentors and domain experts who can help us identify the highest-leverage vertical to enter first. Healthcare billing, legal ops, financial compliance — where does reliability matter most?
Pre-seed conversations with investors who understand infrastructure plays. We're not looking for hype capital — we want partners who value technical depth and operational excellence.
Technical founders who've scaled agent systems. Enterprise operators who've felt the pain. Investors who backed infrastructure winners early (Datadog, HashiCorp, Vercel).
Deep-dive conversations in 2–3 verticals. Identify the beachhead market. Build the relationships that turn into our first design partners and early customers.