Your software records what happened. But it doesn't understand why. And it doesn't learn what to do differently in the future.
Zargar Ink builds AI agent systems that learn continuously — capturing what normally slips through the cracks and giving your team actionable advice in simple language.
Discussion of where and how you're currently using AI (1 day)
Operational audit to identify the biggest gaps (2–4 weeks)
Deployment of a working agent that surfaces live findings from your data (2 months)
System improves as the agent runs and reviews findings (4–6 months)
Regular updates that keep you in the loop and give the AI more to learn from (generated according to your preference)
Most AI tools only look for what you train them to find. They flag what you define as an exception and produce the same outputs over and over without considering how you use them. What we build adjusts its behavior over time — registering every recommendation you act upon, ignoring patterns that look like noise, and surfacing outcomes that generate value more often the longer it runs.
An agent monitors your operational data and identifies issues based on your priorities, like vendor discrepancies or unused inventory. Unlike static rules, it updates its priorities based on what actions you take and what outcomes follow.
The system identifies where your operation loses time and money and sets benchmarks based on your data, not generic industry standards. It recognizes outcomes that generate value and surfaces them more often over time.
Every insight the system produces is written with the person responsible for taking action in mind. No dashboards to interpret. No technical jargon. Just clear findings your team can act on immediately.
High-volume operations generate more signals than any human team can monitor.
A large-scale business sees signals that lead to unplanned downtime, but it isn't able to track those signals manually.
A technology corporation with multiple locations knows that vendor and inventory discrepancies happen, but it doesn't have anyone with the bandwidth to find them.
A distribution company runs into rate discrepancies and billing errors across dozens of vendor relationships, but it can't afford to do manual audits.
Dozens to hundreds of workflow events per day, across vendors, inventory, payroll, or service records.
Your data foundation is already in place. We work with what you have, not against it.
The cost of missed patterns compounds quietly over time. Your team knows it, but can't find them all manually.
The Zargar Ink team combines applied AI and machine learning with clear communication and strategic messaging. So we build systems that are technically rigorous but don't require technical training.
An AI system your team does not understand is an AI system your team will not use. So we emphasize plain-language reporting and documentation that gives your team a clear course of action.
Maurizio brings over two decades of experience building AI and machine learning systems for national laboratories, enterprise technology, and industrial applications. His work spans manufacturing throughput optimization using machine vision and closed-loop control, predictive modeling for medical devices, and AI infrastructure at Microsoft Azure. Maurizio has also built and scaled AI teams at Change Healthcare, Teradata, and Los Alamos National Laboratory. In 2016, he was awarded a Breakthrough Prize in Fundamental Physics.
We start with an operational audit to identify where human oversight just isn't enough. We'll then work to build a system that helps you get the most out of your AI without putting any additional strain on your team.
Reach out and we will reply within three business days. If it's a good fit, we will schedule a time to discuss what your data is currently doing and what it could do.
Or reach us at info@zargarink.com