What we're building
GTM infrastructurethat runs on AI
Four integrated layers that form a complete operating system for go-to-market execution. Signal detection feeds enrichment. Enrichment powers the AI runtime. The runtime generates campaigns. Observability closes the loop.
01
Signal Detection Layer
Identify opportunities before competitors
Scan 47+ sources in real time: funding rounds, hiring spikes, GitHub activity, Reddit intent, SEO changes, product launches, community discussions, churn signals, and buying intent. The system clusters and scores signals so you always know who needs your product — and when.
- Funding round detection
- Hiring spike monitoring
- GitHub & product launch tracking
- Community intent mining (Reddit, HN, forums)
- Real-time signal scoring and clustering
02
Enrichment Pipeline
80+ data points per company
Automatically enrich companies, founders, technologies, social profiles, buyer intent, product usage indicators, and hiring patterns. No more manual Clearbit lookups or spreadsheet stitching — the enrichment layer runs continuously and feeds directly into campaign generation.
- Company & founder enrichment
- Technology stack detection
- Buyer intent scoring
- Product usage indicators
- Automated data freshness checks
03
AI GTM Runtime
Describe a goal. AI handles orchestration.
The core orchestration engine. Users describe goals in natural language — the AI runtime plans campaigns, generates positioning, writes messaging, creates onboarding sequences, deploys multi-channel workflows, and continuously optimizes execution. You focus on strategy; the runtime handles infrastructure.
- Natural language goal input
- Automated campaign planning & generation
- AI-powered messaging and positioning
- Multi-channel workflow deployment
- Continuous performance optimization
04
GTM Observability
Datadog for growth systems
Modern GTM lacks tracing, debugging, and attribution clarity. Zetapad provides full observability: trace every campaign from signal to conversion, debug performance drops, understand channel attribution, and get AI-powered diagnostics when something breaks.
- End-to-end campaign tracing
- Multi-touch attribution modeling
- Performance anomaly detection
- AI-powered campaign diagnostics
- Conversion path analytics
The architecture is the moat
Closed-loop learning
Signal → Campaign → Conversion → Optimization. Every campaign makes the system smarter.
Proprietary signal graph
Over time, we know which signals convert best, which channels activate users, and which positioning wins.
Infrastructure stickiness
Owning attribution, optimization, and diagnostics creates platform-level retention.