Automated outbound
TAM > 20k accounts · LTV > $5K
Volume economics work. Signal-routed sequencing across email + LinkedIn, deliverability engineered first.
case study · omnibound ai
the situation
Omnibound sells AI search marketing - helping brands win visibility inside AI answers, a category that barely existed two years ago. New category, no inherited GTM infrastructure, no playbook to copy, and scale-up expectations on pipeline. The classic answer is hire a marketer, two SDRs, a RevOps contractor, and wait two quarters for the machine to assemble itself.
That answer costs roughly $400K+ a year in loaded headcount before the first meeting is booked. The mandate here was the opposite: one GTM engineer, an AI system, and revenue accountability from day one.
the build
Before any tooling: TAM sized, ICP defined, personas mapped, and 1P/2P/3P signal sources selected - which job-posting patterns, stack signatures, website visitors, and LinkedIn behaviors actually indicate buying intent for AI search marketing. The engine was designed on paper before a single API key existed.
The end-to-end chain below - previously a 2-week deploy - was engineered, tested, and sending in under 3 days. 150+ domains and 300+ mailboxes warmed and orchestrated, deliverability engineered before volume.
Six production workflows - positioning intelligence, the 7-stage copy engine, signal-routed outbound, asset-led ABM, inbound scoring, and the content flywheel - every one wired into CRM instrumentation. Each is broken down node by node below.
Every play attributes to closed-won through a self-built HubSpot layer. BANT qualification, deal-stage hygiene, and Slack alerts keep human attention only where judgment matters: the conversations and the closes. Buyer language from those calls feeds back into positioning - 4 narrative shifts validated in-market so far.
the results · 9 months in
| Metric | Outcome |
|---|---|
| Customers closed | 5 logos · $145K ARR |
| BANT-qualified SQLs | 8 · $264K ARR pipeline |
| Deals in active follow-up | 28 · $633K+ ARR pipeline |
| Positive reply rate | 1.5%+ across 150+ domains |
| Engine deploy time | 2 weeks → under 3 days |
| Research throughput | 6+ hrs/batch → under 30 min |
| Positioning iterations | 4 shifts, market-validated |
the production workflows
These are the actual production systems - Clay, n8n, Python, Claude Code - running across every GTM service line. No slideware; every chain below is live.
trigger: weekly cron + every closed-won / closed-lost deal
→ 4 positioning shifts shipped, each validated in-market before the next
the loop that keeps messaging honest: buyers write the positioning, the system just listens
trigger: new enriched account lands in the Clay table
→ Hyper-personalized copy at scale: 1.5%+ positive replies across 150+ domains
the stages most teams skip are gap analysis and strategic angle - exactly where generic AI copy dies
trigger: job posting, tech-stack change, or LinkedIn engagement detected
→ 40% pipeline-efficiency gain, $180K+ ARR growth in 6 months
fit: TAM above ~20k accounts, LTV above $5K - below that, run ABM instead
trigger: account enters tier-1 target list
→ Enterprise logo closed at $25K MRR
fit: TAM under ~20k accounts, LTV above $50K - the assets do the room-warming
trigger: form fill, demo request, or RB2B-identified visitor
→ Lead qualification cut from 48h to 9.6h (-80%)
speed-to-lead is the cheapest conversion lift in B2B - this buys it permanently
trigger: weekly content cycle + every post published
→ Content stops being a vanity channel: every post becomes a 2P signal source feeding outbound
fit: any B2B motion where the ICP is active on LinkedIn
motion selection
Most GTM waste comes from running the wrong motion for the market shape. The qualification run before wiring anything:
Volume economics work. Signal-routed sequencing across email + LinkedIn, deliverability engineered first.
Every account deserves handcrafted-feeling assets - generated by pipeline, tracked by visit.
Speed-to-lead and scoring first: the cheapest revenue is the demand you already have but answer too slowly.
Founder-led content mined from real buyer language, with engagers captured as second-party signal.
why this matters
Seed-to-Series-B math is unforgiving: the plan says 3x pipeline, the budget says 1.2x headcount. Traditional GTM closes that gap with more people and more handoffs - each handoff leaking context and pipeline. A system-led build closes it with compounding infrastructure: the engine that took 2 weeks to deploy now takes 3 days, the copy that needed review now ships itself, and the research that took an analyst-day runs in 30 unattended minutes.
This was greenfield build number three - after an IT services firm ($180K+ ARR growth, 40% pipeline-efficiency gain) and a MENA cybersecurity startup (zero-to-one ABM engine). The pattern holds across markets: strategy first, system second, revenue attribution always.
open channel
One call to scope your engine. We reply within one business day.