Circes is the Amazon management firm we wished existed when we were running our own brand — so we built it, operating system and all.
Circes was founded by operators, not consultants. We spent years inside Seller Central — building, scaling, and sometimes breaking — Amazon brands. We knew what was missing: a system that could see every lever at once and act on all of them.
Today, Circes runs on a live account every day. Every recommendation, every automation, every result on this site comes from that same account.
Ten years running Amazon brands, including the one I run today. Five of those years I spent inside Amazon, building catalog-risk systems and early LLM tooling. Amazon encourages employees to sell on the platform, so I was the seller and the platform operator at the same time.
I started Circes at MIT as a thesis-in-practice: could the AI systems I helped build inside Amazon be turned outward to give independent brands the operational leverage that only the largest sellers — and Amazon itself — already have? I've been running Circes live on my own brand since day one. Every recommendation it makes for a customer's account has already been tested on mine.
Three years building production agentic AI. At TSBX, he owned the MCP tooling layer for a flagship agent builder platform, cutting model inference costs by 82% in production. That same problem — AI systems that work in theory but bleed money at scale — is one Circes solves for Amazon operators every day.
Before that, he scaled mission-critical supply chain applications at Genpact, cutting service latency by 40%. The overlap with Circes isn't incidental: inventory operations, real-time bid decisions, and multi-agent coordination under production constraints are all supply chain problems dressed in Amazon clothing.
He co-authored a published ACM paper on LLM applications. He joins Circes to architect the multi-agent infrastructure and the Knowledge Ledger — the compounding data asset that makes Circes harder to replicate with every account we add.