Gather AI Secures $40 Million to Bridge the Supply Chain "Reality Gap"

Gather AI, a pioneer in computer vision and autonomous robotics for logistics, has closed a $40 million Series B funding round led by Smith Point Capital. This latest injection of capital brings the company’s total funding to $74 million, signaling a strong vote of confidence in "Physical AI" as the next frontier for supply chain efficiency.

The round was led by Smith Point Capital, the firm founded by former Salesforce co-CEO Keith Block. Participation also included existing investors Bain Capital Ventures, Tribeca Venture Partners, Bling Capital, Dundee Venture Capital, and XRC Ventures, alongside new investor The Hillman Company. The funding will be utilized to scale operations globally and further develop Gather AI’s proprietary "curious" drone technology, which actively hunts for inventory errors rather than passively scanning shelves.

As logistics networks become increasingly complex, the disconnect between digital records and physical reality—often termed the "reality gap"—has become a billion-dollar problem. Gather AI’s platform addresses this by deploying autonomous drones that digitize warehouse inventory in real-time, providing a single source of truth that integrates directly with Warehouse Management Systems (WMS).

The "Curious" Robot: A Bayesian Approach to Automation

At the heart of Gather AI’s success is a fundamental shift in how autonomous systems perceive their environment. Unlike generic scanning solutions that follow rigid paths, Gather AI’s drones utilize Bayesian curiosity techniques combined with neural networks. This allows the drones to behave with a sense of agency, actively seeking out specific data points such as barcodes, lot codes, text, and expiration dates.

This "curiosity" enables the system to make intelligent decisions on the fly. If a label is partially obscured or a pallet looks out of place, the drone can adjust its behavior to capture a better angle or investigate further, much like a human auditor would. However, unlike human workers who can only scan for limited periods, these drones operate continuously with near-perfect consistency.

Critically, Gather AI has differentiated itself from the current hype cycle of Generative AI by avoiding end-to-end Large Language Models (LLMs) for core navigation and identification tasks. By relying on probability-based Bayesian methods, the system avoids the "hallucination" problems that plague LLMs, ensuring that the data fed into supply chain systems is accurate and reliable.

Technical Advantages of Bayesian Curiosity

• Active Investigation: Drones do not just pass by; they "hunt" for missing or anomalous data.
• Hallucination-Free: Uses deterministic probability models rather than generative prediction for data reading.
• Infrastructure Independence: Requires no retrofitting of warehouses (no reflectors, beacons, or special lighting).
• Cold Storage Capable: Operates effectively in sub-zero environments where human labor is difficult and dangerous.

Strategic Backing from Enterprise Software Veterans

The involvement of Smith Point Capital, and specifically Keith Block, underscores the potential for Gather AI to become a standard "system of record" for the physical world. Block, who helped scale Salesforce into a global enterprise giant, views Gather AI not merely as a robotics company, but as a critical intelligence layer for modern commerce.

"Gather AI is redefining how the physical world gets measured, understood, and operated," said Keith Block in a statement regarding the investment. "What the team has built isn't just a better way to count inventory; it's a foundational intelligence layer for the modern supply chain. We believe Gather AI will become the system of record for every warehouse, factory, and yard."

The startup has already demonstrated significant traction. In the past year, Gather AI grew its bookings by 250% and doubled its operational footprint. Its customer roster includes major logistics and retail heavyweights such as GEODIS, NFI Industries, Kwik Trip, Axon, dnata, Barrett Distribution, and Langham Logistics.

Comparing Operations: Manual vs. Gather AI

The adoption of Gather AI represents a paradigm shift from traditional inventory management. The following comparison highlights the operational differences between legacy methods and Gather AI's autonomous approach.

Table 1: Operational Comparison of Inventory Management Methods

Expanding the Footprint of Embodied AI

The fresh $40 million capital will drive Gather AI’s expansion into hundreds of additional facilities across North America, Europe, and Asia. Beyond geographical growth, the company is investing heavily in R&D to enhance the predictive capabilities of its platform. The goal is to move from merely reporting the state of inventory to predicting potential bottlenecks, stockouts, and safety hazards before they impact the bottom line.

Gather AI’s founders—Sankalp Arora, Daniel Maturana, and Geetesh Dubey—met as PhD students at Carnegie Mellon University. Their deep academic roots in robotics have translated into a pragmatic, hardware-agnostic solution that uses commercially available drones rather than custom, expensive hardware. This software-first approach allows for rapid deployment and easier scaling, a key factor in their rapid accumulation of market share.

"Global logistics companies lose billions annually because warehouse activity rarely matches digital system records," said Sankalp Arora, co-founder and CEO of Gather AI. "This 'physical-digital divide' creates operational blind spots. We deliver continuous physical intelligence that eliminates these blind spots."

Creati.ai Perspective: The Rise of Reliable Embodied AI

From the perspective of the broader AI industry, Gather AI’s Series B success highlights a maturing market for Embodied AI—artificial intelligence that interacts with the physical world. While 2024 and 2025 were dominated by the explosion of generative text and image models, 2026 is shaping up to be the year where AI proves its value in industrial applications.

Investors are increasingly distinguishing between "creative" AI, which generates new content, and "analytical" or "physical" AI, which measures and optimizes reality. Gather AI’s success suggests that for mission-critical industries like supply chain, the market favors solutions that prioritize precision and ground truth over generative capabilities.

The use of Bayesian techniques acts as a crucial safeguard. In a warehouse holding millions of dollars in inventory, a 99% accuracy rate is often insufficient; the system needs to know what it doesn't know. Gather AI’s drones are programmed to recognize uncertainty—a trait that makes them safer and more reliable than systems that might "guess" at a blurry barcode. As automation continues to penetrate the physical workforce, this "curious but cautious" architecture may become the standard for industrial robotics.