I define, design, and ship AI products people can understand, use, and verify.

Research synthesis usually strips away its own evidence trail. A summary states something confidently, but checking where that claim actually came from means re-reading everything from scratch.

Drop in a set of documents and SIGNAL extracts entities, clusters related material into themes, and produces a brief where every claim links back to the source text it came from.

A TF-IDF and clustering pipeline groups documents by topic, a rule-based extractor tags named entities, and the brief-generation step is checked against the original source sentences before anything is shown — nothing in the output exists that isn't traceable.

Customer feedback piles up faster than anyone can read it, and by the time someone sits down to summarize it, the specific quotes that actually mattered are already lost.

Upload reviews, interview notes, or support tickets and LENS tags each one — problem, feature request, objection, or praise — clusters similar feedback into themes, and ranks the resulting opportunities by volume and sentiment.

Rule-based category cues and sentiment scoring run on every item; TF-IDF vectors with clustering group related feedback into themes; and each ranked opportunity keeps its original quotes attached, so the priority list can be checked, not just trusted.

Lead intake is usually a black box. A form gets submitted, and what happens between that and a sales rep's inbox is invisible, unauditable, and nearly impossible to improve.

A lead comes in and AUTOMATA validates it, enriches what it can actually infer from the email domain, classifies intent, scores the opportunity, and drafts a response — logging every stage along the way.

Each pipeline stage is a small, independently tested function. A rejected lead stops with a visible reason instead of silently disappearing, and the scoring breakdown lists exactly which signals earned which points — the number is never a mystery.