Building GTM infrastructure for the AI era — activation funnels, PLG signals, and growth loops that compound. The gap between a good product and revenue is always a systems problem.
/ thought process
I read military history to understand systems under pressure. Rommel in the desert. Sherman's march. What strikes me isn't the violence — it's the information architecture. The side that moves signal fastest wins. Not the side with more resources.
I came up through marketing at a Europe-based remote startup and a year on the ground at Vedanta BALCO — one of India's largest industrial operations. Two very different environments, one consistent lesson: the gap between signal and action is where value compounds or dies.
The AI era changed what GTM engineering means. Distribution is a technical problem. Activation is a measurement problem. Virality is a systems problem. I build at this intersection — for Seed-to-Series B SaaS teams that need GTM to scale without burning headcount.
/ active experiments
Paste any SaaS URL. Get back a structured GTM breakdown — motion classification (PLG / Hybrid / Sales-Led), aha moment hypothesis, activation sequence, growth loop identification, and the biggest friction point. Validated on Notion, Linear, and 20+ products. Experiment #1 running: curiosity gap above the email gate.
Builders ship real things. Then send a GitHub link to a hiring manager. Koven fixes that gap. Paste your GitHub URL, X handle, and product link — it reads your commit history, build-in-public posts, and product metadata, then generates a shareable case study page in under 5 minutes. Not a template. Your actual story.
/ gtm engineering stack v1
/ field notes
Operated remotely for a Europe-based team. Built the muscle for distribution-first thinking — running GTM from first principles without a full stack or a large budget. The constraint was the curriculum.
A year on the ground at one of India's largest aluminum operations. Learned how systems actually function at scale — under pressure, with real consequences. The opposite of a startup. Which is exactly why it mattered.