Ziggiz democratized enterprise-grade security for organizations ranging from Fortune 500 companies to local hospitals with one-person IT teams. Security analysts spent nearly three hours daily triaging 4,000+ alerts, with 67% going unaddressed. The team needed to reduce the operational burden of alert triage while accelerating customer onboarding and data integration. Within two weeks of outreach, Ziggiz identified and brought on Richard Abrich, a Principal AI Engineer who had led agentic AI builds for Microsoft and Cleveland Clinic. Over an 8-month engagement, he built a semantic knowledge graph to automate security data engineering work. He also delivered a natural language agent that enabled plain-language queries across the security data. Customer onboarding dropped from 9 months to 5 days. New data format integration shifted from 3–6 months to hours. The implementation automated 90% of security data engineering work, reducing manual effort required to operationalize and use security data.

Sandow’s CEO identified untapped strategic value in years of consumer research data spread across 150+ different sources. The data lived in spreadsheets, Typeform reports, and databases, and it was only linked by phone numbers. Analysis had been manual and typically took weeks. A two-phase system was designed to turn the fragmented research into a usable intelligence foundation. LLMs were implemented to intelligently structure survey responses across sources. Dynamic SQL views were created to support natural-language business questions. In 30 days, a production-ready system was delivered that transformed Sandow’s proprietary consumer intelligence into a B2B market research platform. The unified system reduced analysis time from weeks to real-time responses. This enabled faster access to insights across the previously disconnected datasets.

Traditional video ad production required days of manual work per asset, which limited the ability to test variations. Dicer.ai needed a faster way to generate and evaluate multiple ad concepts without relying on slow, manual workflows. The challenge was compounded by the complexity of visual AI systems compared to more mature text-based LLM approaches. Dicer.ai embedded an AI Engineer with expertise in visual AI systems. The engineer built an end-to-end competitive intelligence pipeline that mapped the market landscape and scraped competitor ads. The system analyzed top-performing ads scene-by-scene and generated multiple video variations. It also added AI-powered performance forecasting to guide which variants to produce. The solution transformed a multi-day manual process into an automated pipeline. It delivered multi-variate video ads in under 2 hours. This resulted in a 90% reduction in ad creation time. The faster turnaround enabled quicker iteration on ad variations.