About Nicholas Iadeluca
Nicholas Iadeluca is the Founder & CEO of ThreatLens AI™ Inc., where he leads the development of vision-AI products that help retailers prevent theft and policy abuse, and unify evidence for faster resolution. His work blends computer vision with the realities of store operations—how teams actually respond, what reliably triggers action, and what scales across fleets of locations.
Alongside ThreatLens AI™, Nicholas works within one of the largest alarm monitoring central stations, giving him day-to-day exposure to high-reliability security operations at national scale. He also works in asset management and investing, applying disciplined, analytical thinking to fundraising, capital planning, and go-to-market—keeping ROI and scalability at the center from pilot to rollout.
Security Operations
Central-station experience: reliability, low-noise alerting, operator UX, and incident handoffs that store teams trust.
Asset Management & Investing
Analytical rigor for fundraising, capital allocation, and ROI—tying product milestones to business outcomes.
Scaling & GTM
Pilot→rollout playbooks, measurable success criteria, and partner-friendly deployments without re-wiring.
Responsible AI
Privacy-by-design: minimum necessary data, secure handling, transparency, and auditability across the lifecycle.
Retail LP Partnership
Co-design with LP teams to ensure alerts are actionable and evidence is unified, time-stamped, and useful.
Operations Mindset
Pragmatic workflows that fit store reality—focus on reliability, speed, and clear outcomes.
Focus Areas
- Self-checkout: item-skip, barcode switching, bagging anomalies with POS tie-in
- Entry & exit: loitering patterns, large-group signals, ORC indicators
- Returns desk: receipt & item verification aids, cross-store appearance linkage
How Nicholas Works
- Pilot quickly with real store footage and clear success criteria
- Tune models per store and measure live results
- Link video with POS context to create unified, auditable evidence
- Iterate with LP teams; scale without re-wiring
Exploring a results-driven approach to AI for retail?