Company
Ziggiz democratizes enterprise-grade security by automating the data engineering burden that traditionally cripples security operations. The company serves organizations ranging from Fortune 500 companies to local hospitals with one-person IT teams, making sophisticated security intelligence accessible to users without technical backgrounds.
The Challenge
Security analysts spend nearly three hours daily triaging over 4,000 alerts, yet 67% go unaddressed — buried under overwhelming volume and complexity. Ziggiz had built sophisticated security infrastructure for large organizations, but needed to make this technology accessible to users across the technical spectrum, from Fortune 500 companies to local hospitals with one-person IT teams. The requirements were exacting: they needed deep AI expertise with practical understanding of which tools to use when, healthcare domain knowledge to ensure the platform addressed industry-specific constraints, and experience building similar systems at scale. As an early-stage startup, Ziggiz couldn't sustain the burn rate of a full-time hire but needed someone who could deliver within their aggressive timeline. Without solving this, critical security intelligence would remain locked behind manual processes, leaving vulnerable organizations—especially in healthcare where lives hang in the balance—exposed to catastrophic threats.
The Solution
Within two weeks of Ziggiz's outreach, A.Team identified Richard Abrich—a Principal AI Engineer who had led agentic AI builds for Microsoft and Cleveland Clinic. Abrich integrated into Ziggiz's team from day one, joining standups and collaborating directly with leadership. Over eight months, he built two interconnected systems that made enterprise security accessible to non-technical users. He started with a semantic knowledge graph that automates the 90% of security work that's just data engineering. The system builds semantic understanding of data relationships, enables sparse field extraction to process only the data actually being used, and orchestrates information for both human and machine consumption at massive scale.
He then created a natural language agent that enables users to interact with complex security systems in plain language, asking questions like "What does this alert mean and what should I do?" or "Show me the top five users with the highest risk" and receiving responses clear enough that a surgeon could understand what to do. This wasn't a simple chatbot—Abrich applied advanced context engineering techniques to carefully control how information accumulated across conversation threads, preventing hallucinations and ensuring security teams could trust the guidance they received
Technologies Used
Natural Language Processing: Built an AI agent enabling non-technical users to query security systems and understand alerts in plain language
Context Engineering: Applied advanced techniques to manage information flow across conversation threads, preventing model hallucinations
Semantic Knowledge Graphs: Created an intelligent layer that understands data relationships and determines what's worth processing
SVMs and Gaussian Curves: Employed for data clustering and statistical analysis within the processing pipeline
Sparse Field Extraction: Processed only actively-used data to dramatically reduce computational costs and improve efficiency
LLMs: Integrated large language models as part of a carefully selected toolkit matched to specific challenges
"How do you get access to exceptional talent that understands it's not about the tool, but how you use it? A.Team can get the right person for the job to help you get to your next stage of growth."
— George Webster, Founder & CEO, Ziggiz
The Results
Ziggiz transformed from a platform requiring nine months to onboard customers into one that delivers enterprise-grade security in days, making sophisticated threat detection accessible to organizations that previously couldn't afford the expertise or infrastructure.
Customer Onboarding
Accelerated from 9+ months to 5 days—a 99% reduction—by automating complex data processing and semantic understanding.
Data Integration Speed
Reduced new format integration from 3-6 months to hours through automated processing and intelligent data orchestration.
User Accessibility
Expanded from technical experts only to non-technical users including hospital IT members who can now understand and act on security alerts.
Competitive Positioning
Strengthened core intellectual property with a scalable, automated pipeline that formalized previously manual processes in ways difficult for competitors to replicate..
