Your pipeline runs on someone
remembering to do it.
I build the system that finds the accounts worth talking to, reaches them in their own words, and tells you which variable to change next. Fixed scope, fixed fee, and a guarantee on the first step.
It is almost never the copy.
It is what feeds the copy.
The list went stale
Someone built a good list once. Nothing refreshes it, so outreach is running on companies that were the right fit three months ago. The pipeline does not stop loudly. It just quietly stops being worth working.
Personalisation nobody has time for
The version that works takes ten minutes a contact. So it gets skipped on a busy week, the send goes out generic, and the reply rate gets blamed on the copy instead of the research that never happened.
Sending into the void
Nobody is checking placement. A chunk of it never reaches an inbox at all, so every number downstream is measuring a smaller send than you think you made.
No idea which variable moved
The list, the subject line and the offer all changed in the same week. Something shifted. Nobody can say which one, so the next test starts from scratch.
Real projects.
Including the ones I told them to stop.
An always-on outbound engine, from signal to sent
What they wanted was steady flow of leads worth talking to, going out without anyone babysitting it. Sourcing and validation run on crawl4ai, Tavily and Firecrawl, so a lead only counts once a real page says so. Triggers stay on in the background through PredictLeads and Companies House, funding, hiring and filing signals, so the list refreshes itself instead of waiting for someone to rebuild it. Each contact gets personalisation written off that research, then goes out multichannel through HeyReach and Smartlead. Everything reports into one dashboard, which is the point: you can see which variable moved reply rate and change that one next. Two things had to be fixed before any of it could scale. Their first wave was landing around 45 percent in the inbox, and the research grounding the personalisation was requesting a pricing page on every domain and saving the 404 pages as company intel.
- 830 lead verified engine, 1.1 percent bounce
- 45 to 100 percent inbox placement
- Research grounding 36 to 92 percent
A partner channel built from nothing, then the system to run it
Owned growth end to end. Researched 119 communities across 11 countries, contacted 57, ran live activations with the strongest of them. When the numbers said cold email was the wrong pipe for a low ticket product, I re-aimed the channel to direct messages instead of scaling a loser. 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 researched, 82 contacted, 10 calls booked
- Campaign OS live, three campaigns launched
Every system here has something that can say no
Seven systems running a piece of a business on their own. A content engine that cannot publish a number you never said. An outreach engine that physically cannot invent a client result. An application pipeline locked to a truth file so a language model writing about my career cannot claim something I did not do. The guardrail is the product.
- About 240 hours removed
- 12 hours a month, measured
- 11 hard stops across seven systems
I ran the outbound agency before I automated it
Built Terrush from zero: the CRM, the sequencing, the lifecycle automation, the list. Self served, no support team. Everything I now build for other people, I first had to run by hand at volume, which is the reason I know which parts are worth automating and which parts are just busy.
- 50,000+ prospect list
- About 6,000 sends a month
- 5 industry cohorts, no team
- Measurement
A consumer brand's email open rate looked healthy. I proved 96 percent of those opens were bots and privacy proxies, so the true human rate was about 4 percent. Every decision before that had been made against a number that was not real.
- Knowing when to stop
I built that brand's cold email infrastructure from scratch, sent 486 emails and got 2 replies. I recommended killing the channel rather than scaling it. That recommendation was the deliverable.
- Verification
I audited an AI research tool against ground truth on a ten company sample and measured roughly a 10 percent hallucination rate. It was replaced before it wrote anything a client would see.
Client names are withheld until they are happy to be named. Every number above comes from work I did and can walk you through.
Not GTM: ScrolliQ, an iOS app → Three coursework questions before Instagram opens. On TestFlight.
Three steps.
You can stop after any of them.
The diagnosis
750 EURA structured 45 minute call through what actually happens between a company becoming a fit and someone replying to you. Then a written report: where the list goes stale, where the research is thin, where sends are not landing, and which one to fix first.
Guarantee: if I cannot find at least three fixable leaks in how you source, send or follow up, you pay nothing. The fee credits toward the first build.
The build
1,000 to 3,000 EURI build the thing. Scoped up front, fixed fee, paid on delivery. Usually always-on triggers into a verified list, research that grounds the personalisation, multichannel sending, and one dashboard that says which variable to change next.
The machine is built to produce conversations. You still pay for the machine, at a fixed price, not a share of what it produces.
Ongoing, only if it earns it
monthlySome clients keep me on to build the next thing and maintain what exists. This starts after something has already worked, never before.
No lock in. If a month has nothing worth building, we skip it.
- Quote you a number of meetings. I build the system that produces them and charge for the system, because that is the part I control.
- Sell you a tool you do not need. Most of what I build runs on software you already pay for.
- Scale a channel that is not working. If the numbers say the pipe is wrong, I will say so and we re-aim it instead.
- Let a model write something it cannot back up. Every claim in an email traces to a page that was actually read.
Systems I’ve built and documented.
The AI GTM Stack for Solo Founders
The full playbook, the four AI systems I run to do go-to-market without a team. Outreach, qualification, content, strategy. Built in Claude Code.
The complete stack →The Research-First Stack
Claude Code outreach pipeline I used to close my first B2B consulting client. Apollo → Tavily → Claude → Loom. All six files included.
138 → 11 → 1 · 9% close rateWhere does yours stall?
Tell me what happens between a company looking like a fit and someone replying to you, and where that breaks. If the answer is nothing worth building, I will say so and we both save an hour.
vishwahithhmora@gmail.com