A warehouse AI startup is betting that the cheapest capacity in logistics is the capacity facilities already own but lose in seven-minute increments. Seeteria, founded by CEO Sarit Tamir, connects its analytics platform to a warehouse's existing camera network over Wi-Fi — no new sensors, no hardware installs, no system integration — and flags idle dock doors, blocked aisles, forklift congestion and overloaded staging areas in real time.

The commercial argument rests on compounding. A dock door sitting idle for seven minutes, a forklift rerouted around a blocked aisle for twelve, a staging area jammed for fifteen — each incident looks trivial in isolation, and operations managers routinely dismiss them as such. Summed across dozens of doors and dozens of forklifts over an eight- or ten-hour shift, the losses add up.

"When you sum them and they add up into hours of lost capacity every shift," Tamir said.

Tamir has spent more than a decade building AI and computer vision systems for logistics, warehousing and manufacturing. Her framing of the value proposition is deliberately narrow: squeeze more throughput from resources the operator is already paying for, rather than sell new capital equipment.

"Our goal all the time is to get the warehouse more capacity from the resources that they are already paying for," she said.

Three tiers of visibility

The platform serves three distinct user groups. Floor supervisors get live push alerts on a mobile or tablet app, with each door and zone displayed in a color-coded status — green, yellow or red — so they know exactly where to send people. Warehouse managers receive a post-shift summary of bottlenecks to improve flow shift over shift. Executives see a financial-impact view tied to metrics such as detention fees and missed throughput targets.

The design of the supervisor tool reflects a specific operational reality: warehouse supervisors walk roughly eight miles per shift. Tamir positioned the app as a way to reduce that burden, not to replace the worker.

Blind to people, not to pallets

Privacy is a deliberate constraint, and one the company uses to defuse one of the more contentious issues around AI on the warehouse floor. The system does not identify people. It identifies objects, movement, zones and events — forklifts, pallets, dock doors, congestion patterns.

"The system is completely blind to people," Tamir said, adding that the decision stemmed from a commitment to worker privacy and an effort to reduce anxiety about AI surveillance among floor staff.

For shippers and 3PLs weighing computer vision investments, that distinction matters: deployments that track equipment rather than employees face a materially lower friction barrier with workforces and, in many jurisdictions, fewer compliance complications.

Pilot pipeline

Seeteria is actively recruiting U.S. pilot partners. The minimum threshold Tamir cited is a facility with at least eight active dock doors and a meaningful forklift fleet. Smaller operations, she said, are unlikely to generate sufficient return on the deployment.

The company recently completed a stint at the CoLab accelerator in Chattanooga, Tennessee — a connection made at the Home Delivery World conference in Nashville — and has announced a new pilot launch in Chattanooga is imminent.

The name itself derives from Soteria, the Greek goddess of safety, with the spelling changed from "SO" to "SEE" to reflect the computer vision focus — an origin Tamir traced to a family visit to the Vatican in Rome.

For mid-size and large distribution operations, the model points to a broader trend in warehouse technology: the marginal cost of visibility is falling toward zero as analytics layers attach to infrastructure that already exists. If Seeteria's Chattanooga pilot demonstrates measurable throughput recovery without capex, expect the no-new-hardware pitch to put pricing pressure on sensor-based competitors.