GTM operatorThree trainings. One operator.
engineer who ships, strategist who models, marketer who positionsOutbound, enrichment, and the agentic pipelines underneath them. Built and run end to end, not just configured.
GTM operatorThree trainings. One operator.
engineer who ships, strategist who models, marketer who positionslead outbound engine, rebuilt for a B2B consultancy.
brand partnerships closed, from a channel built from zero.
of manual work removed across seven systems I built.
0000001Steady flow of leads worth talking to, sent without anyone babysitting it. Sourcing and validation on crawl4ai, Tavily and Firecrawl, so a lead only counts once a real page says so. Funding, hiring and filing triggers stay on in the background through PredictLeads and Companies House, so the list refreshes itself. Personalisation is written per contact off that research and goes out multichannel through HeyReach and Smartlead, reporting into one dashboard so you can see which variable moved reply rate and change that one next. Two defects had to go first: their first wave landed around 45% in the inbox, and the research grounding the personalization was saving 404 pages as company intel.
830-lead verified engine · 1.1% bounce · 45% → 100% inbox placement · grounding 36% → 92%

Owned growth end to end. Researched 119 communities across 11 countries, contacted 82, booked 10 calls and closed 4 brand partnerships. When the numbers said cold email was the wrong pipe, I re-aimed the channel to direct messages rather than scale a losing one. Then built the campaign platform underneath it: eight agents that ship a branded campaign, two of which exist only to stop a launch that is not ready.
4 brand partnerships closed · 119 communities across 11 countries · campaign OS live
The systems behind it→
Eight self-built scrapers across the EU market feed a scoring pipeline, a CV generator locked to a truth file, and a deterministic gate that blocks anything under threshold. The interesting constraint was not the scraping. It was making a language model that writes about my career structurally unable to say something I did not do.
6,760+ jobs sourced · 107 tailored CVs shipped · 105 applications sent

MMSC at IE Business School (current). Building Founder Copilot in private alpha. Madrid.
Growth at OLEUS, an endurance sports nutrition brand. Built the community partner channel from zero and closed 4 brand partnerships through it, opened retail across 4 European markets, and shipped an 8-agent pipeline that runs a branded campaign end to end.
Growth and GTM at Exec.Education. Took an unproven hypothesis to a live acquisition system: brand, messaging, site, outbound and analytics, then ran the experiments against real traffic. 14,900+ impressions from a standing start of one follower, and a narrowing to one ICP and one metric once the B2B route failed to reach decision makers.
Master in Management at IE Business School. IE Foundation Scholarship, top 10% of applicants. Externships at HP Tech Ventures (Axiom Cloud memo, $20B market) and Beats by Dre (100+ Gen-Z surveys → messaging frameworks).
Founded Terrush, a B2B lead-gen agency built on n8n + Sheets. 50,000+ prospect list, 5-step cold-email sequences at ~6,000 sends/month across five industry cohorts. Self-served, no team.
Founded Xploreee, an eco-friendly travel clothing brand. 3-piece collection, market research across 5+ Indian cities, 20+ founding-customer interviews.
Mt. Kilimanjaro. Khardungla Pass. Meerathang Glacier. Top 5% in a 28-day mountaineering course. Best decision-making training I’ve had outside of shipping product.
I build the AI systems, I don’t just configure them. If you have a pipeline problem and nobody in-house who can build the machine to fix it, that’s the job I want.
Happy to send a version tailored to the role. Madrid, or remote across the EU.
Not hiring? Consulting works differently, diagnosis first, then builds. Founders can find the product at Founder Copilot. Everything else: @vishwahithh.