AI-powered workflows combined with human operators to build pipeline, qualify opportunities and book meetings that convert.
Most GTM teams run campaigns in silos, rewrite copy every Monday, and hope something converts. We replace that chaos with repeatable workflows - AI handles research, enrichment and delivery at scale, while human operators shape strategy, qualify replies and book meetings that actually show up.
Sniffs cross-channel intent, hiring waves, and financial triggers before they surface.
Frames structured hypotheses and segment-specific angles worth testing this week.
Orchestrates multichannel delivery across warmed, isolated sender infrastructure.
Qualifies responses, defuses objections, and books meetings that survive the show-up rate.
Decides which experiments compound and which get culled on outcome density.
Six composable planes replace the stack of point tools most GTM teams glue together. Each layer emits typed data downstream and receives learning upstream.
This is how the Hound decides who to work today. Stack the signals you'd bet on for your market - the fit score, the gate, and the outreach angle recompose in real time.
Add at least one signal - the system needs something to reason about.
Same pod, same headcount. Every week feeds the next: sharper ICP, sharper angle, more meetings out the other end. Set your baseline below - the chart shows what week 12 looks like.
Novel category, many possible ICPs, unclear angle-market fit.
Crowded verticals where messaging saturation kills conversion.
Multi-client outbound requiring per-account hypothesis isolation.
Technical buyers who reject marketing and reward operator language.
Long sales cycles that demand thesis-driven, signal-timed outreach.
Scrub any input on the left - or click any number in the funnel on the right to edit it directly. Rates back-solve, revenue recomputes, everything stays in sync.
Three engagements, three shapes of buyer, one operating model. Names withheld under NDA - numbers are real.
Category was new, ICP was fuzzy. We modelled 6 hypotheses in parallel, killed 4 by week 3, and doubled down on the two that replied. Meetings went from 6 → 38 / mo without touching headcount.
Their old sequences read like landing pages. We rewrote in engineer voice, timed sends to release-cycle signals, and let the pod reply in-thread. Positive-reply rate tripled inside 4 weeks.
Long cycles, few accounts, expensive to be wrong. We tracked funding, hires and stack changes across 800 targets and only reached out when 2+ signals stacked. Every booked meeting was in-ICP.
Monthly meetings with in-ICP prospects - qualified, not just booked.
Precision at identifying high-intent accounts before they enter a funnel.
Rate at which new GTM experiments reach statistical significance.
Cost reduction per validated ICP meeting across a six-month deployment.
We don't sell software - we compose it. Each partner slots into a specific layer of the loop: data, enrichment, delivery, or translation. Swap any component; the system keeps running.
↳ Logos belong to their respective owners. Stack composition tuned per engagement.
The Hound runs your first search loop within 48 hours of deployment. You bring the ICP hypothesis. It brings the experimentation infrastructure.
