Four products, idea to production — founder or lead engineer on each.
The anti-drift framework behind how I ship AI-native software: an MCP server that gates autonomous coding behind hard, blocking quality checks — with a 7-phase workflow ledger and persistent memory on a Rust engine substrate. The gated Ralph loop that keeps agents on-task instead of drifting; the reliability method the other builds run on.
2–4×
AI-search visibility engine: continuous monitoring pipelines — controlled LLM querying, crawlers, analytics — that detect model-response drift, competitive displacement, and visibility loss. Drove 2–4× AI mention coverage in controlled deployments.
1,600+
Applied-AI SaaS that generates, validates, and scores business ideas on market, compliance, and feasibility — OpenAI Responses API, multi-stage pipelines, pgvector semantic retrieval. 1,600+ users, validated by paid conversions.
Pre-transaction fraud detection for SaaS: client-side signals, real-time analytics, and server-side logic that catch velocity spikes and abnormal interaction patterns before downstream processing.
Senior research scientist — compute- and simulation-heavy systems in a PhD-driven R&D environment.
Bridged research prototypes and production for high-value federal programs (tens of millions in awarded contracts): parallel computation via web workers and high-fidelity visualization — ~38% faster load on large-scale spatial and simulation data.
I take LLM products from idea to production, and build the reliability layer that keeps them working — the same anti-drift engineering behind OpenSquid. One engineer, idea to ship.
engineer to engineer — no recruiters, no decks