Case Studies
Work
What we've built, and what it changed operationally.
From Spreadsheets and Email to Their Own Platform in 8 Weeks
The Problem
The team managed carrier issues across Google Sheets, email, and WhatsApp. No history, no traceability, no metrics by carrier. Every issue was resolved in 90-minute meetings with incomplete information and no record of who did what. Measuring carrier performance was impossible.
What We Built
Two weeks to a working AI POC. One month of internal testing. By month four, the main carriers and forwarders — Sinotrans, DHL, Semaco, Triviel — were operating in production. External development cost estimated at USD 43,000–200,000 with an 8–12 month timeline. McKinsey estimates fewer than 10% of AI projects reach production. This one did in eight weeks.
The Result
Meetings dropped from 90 minutes to 30. Carrier response time fell below 24 hours with a defined SLA. Asian suppliers now access the platform without VPN. The team has complete history, traceability by carrier, and a full action log for every issue.
90 → 30 min
Meeting duration
< 24h
Carrier response time (defined SLA)
8 weeks
Zero to production (vs. 8–12 months industry standard)
"iTracker proved that the team can build high-impact tools in weeks — not months."
The Manager Who Got His Time Back
The Problem
The manager manually processed dozens of emails every day — each following the same logic: check if the information was complete, determine if the response applied, draft the reply. Five minutes per email. High-value time lost to repetitive, low-judgment work.
What We Built
In one week, we automated the entire flow. The system reviews incoming email, applies the business rules, and generates a ready-to-send draft. The manager reads, adjusts if needed, and hits send.
The Result
One week of work to recover hours of management time each month. The manager stopped being a typist and went back to being a decision-maker.
1 week
To automate the entire flow
5 min → 0
Per email processed manually
"A process that seemed complex had a simple logic. It just needed to be built."
From Participant to Builder
The Problem
Gonzalo arrived at the workshop not knowing what to expect. Like most people, he was using AI in a basic, unsystematic way — familiar with the tools, not fluent in applying them.
What Happened
Weeks after the workshop he was building his own assistants. Then Notion tools. Then he proposed creating an internal innovation lab. Months later, unprompted, he launched an app showing the real-time global journey of the company's containers. Today he has 7 agents in production — from logistics problem analysis to API integrations — with new projects connecting his lab directly to iTracker.
The Result
An analyst who went from passive user to active AI infrastructure builder. Not because he was told to — because the workshop gave him the language, the method, and the confidence to start.
7
Own agents in production
"I thought I knew AI and realized I was behind. Now I'm a machine." — Gonzalo, Innovation, LI
If you're running into a similar operational problem, the first step is usually a process audit — not a tool conversation.
Start with a Strategic Clarity Workshop