Every year, $20T of U.S. freight changes hands on the strength of a paper Bill of Lading and a quick nod at the dock door. When thieves strike, a $250k load can vanish in minutes.
Indemni is turning that soft underbelly of global trade into a strength. We're shipping the first real-time identity & document verification platform purpose built for logistics. Think Stripe's risk engine, but for cargo moving through warehouses, ports, and yards all over the world.
Started by operators from Flexport and DoorDash, we're already piloting with warehouses and carriers across the US who move some of the most valuable cargo between warehouses, airports and ports (think national security level importance such as server racks, GPUs, electronics, etc.).
JS TS / React / React Native (Expo) / Node/Express / Supabase (Postgres Edge Functions) / Python (ML)
Bias for action. “Ship > perfect” resonates, but you still write the unit test (the stakes are high)
Example: Warehouse has a new requirement for verifying Hazmat documentation in the check-in flow. How fast do we get that live?
Product intuition. You ask why before how, and think first and foremost in problem statements (what problems am I solving and is this a problem worth solving?)
Example: Freight Broker is asking if we can auto-translate documents into different languages. Is this something we prioritize now based on why we think this is important, and how do we fit this in our roadmap and the way we build in the future?
Comfort everywhere in the stack. Wrangling SQL in the morning, and debugging API logs in the afternoon are a breeze for you.
Example: Driver is having trouble checking into the app. How do we handle a live edge case of a driver using a flip phone in 2025?
5 yrs building distributed systems in Node/Express, serverless, or similar.
Example: If we're handling X number of verifications a day, what should our short-term, medium-term and long term stack look like and how do we balance that against intensive vs. non-intensive tasks (i.e. pinging APIs for verification vs. lookup government databases for compliance).
Nice-to-haves: Logistics, WMS/TMS, Fraud/Risk ML experience, computer-vision pipelines, or hardware-adjacent experiences (RFID, scanners, etc.).
Example: If you've ever heard of Extensiv or a scanner gun before.