Gather AI is tackling the physical-digital divide in logistics by delivering continuous physical intelligence that aligns real-world warehouse activity with digital records, reducing blind spots, bottlenecks, and margin erosion.

Its system relies on proprietary, real-world warehouse data—millions of images—to train vision-based models that let drones and existing mobile equipment see, count, and verify inventory in complex environments without needing new infrastructure.

The Physical AI platform continuously observes and verifies inventory and movement using drones and already-deployed devices, aiming to replace periodic manual counts and minimize blind spots.

Key executives include CEO and co-founder Sankalp Arora, with investor insight from Keith Block of Smith Point Capital Management, and recognition from CB Insights and the Inc. Power Partner Award.

The current material centers on the funding announcement and product focus, presented like a press release rather than a full feature article.

Gather AI doubled its operational footprint and achieved 250% growth in bookings over the past year, with the platform adopted as the operating standard by major logistics and manufacturing customers such as GEODIS, NFI Industries, Kwik Trip, Axon, dnata, Barrett Distribution, and Langham Logistics.

The company raised a $40 million Series B led by Smith Point Capital Management, bringing total funding to $74 million, with participation from Bain Capital Ventures, Tribeca Venture Partners, Bling Capital, Dundee Venture Capital, XRC Ventures, and The Hillman Company.

The fresh funding will fund expansion to hundreds of additional facilities globally and scale engineering and customer success teams to support enterprise deployments, with plans to showcase at Manifest and MODEX in 2026.

Expansion targets include North America, Europe, and Asia as Gather AI scales, aiming to offer proactive inventory management through predictive capabilities.

CEO Arora describes the funding as enabling a shift from real-time visibility to autonomous orchestration, turning Physical AI into an operating system for modern logistics.

The technology operates at the edge using Bayesian methods with neural networks, avoiding reliance on end-to-end large language models to support decision-making for next actions.

Customers report 99.9% inventory accuracy, up to 80% reduction in manual counting, and a 5x productivity boost, with ROI typically under six months.