Managed OutboundFor · AI infrastructure
In a market this noisy, precision is the only motion that works.
Every AI infra startup is doing outbound. Almost all of it is generic. We focus on the signal layer that separates pipeline from spam: actual model usage, hiring patterns, and real evaluation activity.
What you're up against
Outbound pains specific to ai infrastructure.
- Buyer titles are still being invented (AI engineer, ML platform lead, GenAI product manager).
- Inboxes are saturated with generic 'AI-powered X' pitches.
- Stack changes weekly: targeting decays in days.
- Open-source motion makes lines between user and buyer blurry.
The motion
How we run outbound for ai infrastructure.
Compound title and signal targeting
We do not rely on Apollo titles alone. We layer GitHub activity, repo contributions, model evaluations, and hiring spikes.
Weekly signal refresh
AI infra signals age in days. The pod refreshes lists weekly, not monthly.
Technical credibility in copy
Sequences reference actual model architectures, inference patterns, or evaluation methodologies. Generic language gets filtered.
Multi-touch motion
Buying committee includes engineering, product, and finance. We coordinate touches across all three with consistent positioning.
Signals that convert
The signals that work in AI infrastructure
- GitHub activity on relevant repos (forks, stars, PRs)
- Hiring for ML engineer, AI platform, or RAG-related roles
- Open-source model adoption signals (HuggingFace, etc.)
- Conference participation (NeurIPS, ICML, AI Engineer Summit)
- Public model card publications or benchmark submissions
- Funding rounds with AI infra mentioned in the press release
FAQ
Frequently asked questions
Next step
Run outbound built for ai infrastructure.
90-day pilot, month-to-month after. No long contracts.