Principal at Craft Ventures
Check size: No Dan-specific check range is public. Craft invests across early and growth stages through separate venture and growth funds; Dan's official profile emphasizes helping early-stage founders, while recent attributed investments range from Seed through later-stage rounds. Confirm current vehicle and sizing through inquiries@craftventures.com.
Dan backs early-stage B2B and vertical software, cybersecurity, infrastructure and physical-AI companies where a strong product insight can become a scalable operating and go-to-market machine. His investing lens is unusually operator-led: he underwrites customer pain and technical differentiation, then tests whether the team can segment its market, allocate capital rigorously, build repeatable distribution and move from early traction to predictable growth.
Use Craft's official inquiries@craftventures.com channel and reference Dan's exact /in/dan-moor-020b1428 profile; no personal submission route is published. Lead with the urgent customer problem, technical insight, stage and round, and why this team has earned the right to solve it. For enterprise or cyber, include customer segmentation, ACV, sales-cycle and win-rate cohorts, retention, pipeline source, CAC payback, net sales efficiency and partner-channel leverage. For AI/security, explain the proprietary data loop, inference architecture, real-world reliability and how the product prevents or validates risk rather than merely generating alerts.
Founder-market fit grounded in direct operating or technical insight; category-defining enterprise products with urgent pain; customer evidence that shows trust and repeated use; proprietary data or feedback loops that compound; architectures with clear technical advantages; and teams capable of converting broad horizontal opportunity into a precise ideal-customer profile, repeatable distribution and strong unit economics. He is especially equipped to help with strategy, finance, operations, fundraising and GTM execution.
No personal pass list is published. His current writing points to weak fit when a horizontal product claims to serve everyone without segment economics, when growth cannot be decomposed by customer cohort or channel, when AI adds cost or attack surface without a defensible technical loop, when enterprise claims lack trusted user evidence, or when a company tries to scale enterprise sales before velocity, predictability and efficiency are measurable. These are attributable diligence signals, not quoted hard exclusions.
Evaluate a growth engine through efficiency, velocity and predictability; a compelling financing narrative needs evidence on all three rather than a single blended growth metric.
Compare selling price with sales-cycle length and complexity by customer segment and channel to identify where each unit of GTM effort produces the strongest return.
Measure each marketing and partner channel by pipeline and closed-won return, fix or reduce underperformers, and reallocate capital to the channels that demonstrably compound.
Prefer security systems that prove what is exploitable, remediate it and verify the fix repeatedly, using production feedback to improve faster than AI-enabled attackers.
For high-stakes AI, validate that sources, architecture, latency and privacy earn trust exactly where a professional makes decisions, not only in benchmark demonstrations.
Underwrite whether investor operating expertise can materially accelerate a company through hands-on help with GTM, finance, capital allocation, operations and fundraising.
Machine-speed attacks require continuous autonomous validation; Horizon3's breadth, purpose-built architecture, real-world cyber-terrain dataset and production trust create a compounding advantage.
A durable cyber GTM engine should be evaluated through efficiency, velocity and predictability, with segment-level difficulty ratios, unit economics and channel returns guiding resource allocation.
On-device language models can shift enterprise security from reactive alerts to real-time, intent-aware prevention while preserving latency, privacy and data sovereignty.
A reusable developer-infrastructure layer can remove the months-long enterprise-readiness detour and let software teams unlock large customers without diverting core engineering resources.
Purpose-built clinical AI can earn durable adoption by grounding answers in peer-reviewed sources, fitting clinician workflows and demonstrating exceptional trust and daily utility.
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