Think. Model. Build. Ship.
I'm Pouyan Jahangiri — AI architect and engineering leader, with roots in mechanical engineering and applied math. I build AI that survives contact with production: retrieval, knowledge graphs, agentic services, and the ML platforms under them — recovering the hidden structure in messy real-world signal and shipping it.
Retrieval, recommendation, RAG — under the hood it’s one question: given a point, which others are closest? Here are 120 points. Grab the orange one and move it.
Predict a company’s total, and predict each region — they rarely add up. My patent reconciles the levels. Drag the trust dial: at 0% you trust the regions; at 100% you force them to match the top-line total.
A deep-agent harness doesn’t answer in one shot — it decomposes. Each step can spawn 3 sub-agents and recurse 2 levels deep. Drag across the tree to follow one trajectory, from the task down to a tool call.
On-device inference skips the round trip — but a small device is slower per FLOP. Set the network round-trip to 60 ms and drag the model-size marker. Left of the crossover, ship it to the edge.
Finding hidden structure started literally — modelling particles at a national accelerator — and kept reappearing: in knowledge graphs, in forecasting, in agentic systems, and now in building companies around them.