Project case study
Injester
liveAgentic commerce intelligence. AI agents are already window shopping your store, and they skip what they can't confirm. Injester shows merchants what the agents miss, then retests after the fix.
AI agents don’t impulse buy. They buy what they can verify.
They’re already window shopping. A shopper asks their agent for machine-washable linen pants with a tall inseam. Your pants fit. But the inseam isn’t published per size and the care label is an image. The agent can’t tell, so it shortlists a store that can.
Injester runs that test for the merchant. We turn a store’s products into real shopper requests and run them against personal agents. Every miss comes with evidence. The merchant approves fixes built on their own product facts, and we retest to show what changed.
Test. Fix. Retest.
On the other side of the counter, a Commerce API for the people building the agents.
It started at Nebius.Build SF in March, my first hackathon. The first prototype stripped a web page down to what an agent actually needs: 81% less content, 3 to 5x better task completion. It won, and we demoed it at NVIDIA GTC the following Tuesday. Same lesson, new surface. Agents act on what they can read and confirm.
The launch film tells it in 90 seconds.
Be the store agents choose. injester.com