Shipsy launches 'Shipsy Brain', a logistics intelligence layer for enterprise. Built on more than 1.5 billion logged decisions and specialized open source models, Shipsy Brain enables enterprise AI agents to act with context. Shipsy today announced the beta launch of Shipsy Brain, a logistics native intelligence layer. Shipsy Brain helps enterprises orchestrate decisions and execute operational workflows with considerably greater accuracy, speed, and control over frontier AI models "AI in logistics must understand how shipments, drivers, documents, carriers, contracts and many other variables interact with each other and then take the right action. Shipsy Brain brings this operational depth to global supply chains. It is built to help enterprises move beyond dashboards and copilots toward AI systems that can reason, recommend and act within clearly defined business controls." said Soham Chokshi, Co-founder and CEO of Shipsy. What sits behind Shipsy Brain. It combines specialised open source models with logistics data generated through Shipsy's platform. That includes: * 50 billion+ operational eventsrecorded over five years. * 1 billion+ autoassigned decisions and 500 million+ human decisions, along with reassignment history. * 100 billion+ GPS location pingsconnected with route and delivery outcomes. * 3 billion+ delivery labelsacross different carriers and formats. * 342 carrier integrationsand more than 50 million routing decisions. * 3 billion+ hub scanscovering the movement of shipments through the network. * 5,000+ workflowsbuilt across Shipsy's Workflow Builder and AgentFlow platform. This data enables Shipsy Brain to understand logistics specific context that may be ambiguous to a general model. Shipsy Brain is designed as a central intelligence layer coordinating specialized models for areas such as documents, consignments, trips, workflows and finance. These models can power agents for document validation, address intelligence, anomaly detection, ETA prediction, routing, settlement management and workflow recommendations It lives inside Shipsy's AgentFleet platform, where it watches live operations, identifies manual work it can automate, requests permission, and self-executes gated by its confidence on each specific action. When a human corrects it, it learns from the correction, so the platform improves from what it runs. Also as it is a context layer, it is model agnostic. Any model connected to it gains access to the full operational context which means every general improvement in AI models makes Shipsy Brain more useful. How does Shipsy Brain drive impact On accuracy, Shipsy Brain is built on proprietary knowledge from more than 5 billion shipments, billions of platform actions, and thousands of logistics workflows. This allows it to interpret domain-specific nuances such as different names for consignment numbers, that generic models often miss. In document-intelligence benchmarks, the Shipsy model's score improved to 86.6%, while an earlier Gemini comparison achieved 63.4%. On speed, Shipsy Brain uses fine-tuned, logistics-specific models that already understand industry terminology, workflows, and operational context. Unlike general-purpose frontier models, which must process extensive prompts to understand each task, these specialized models can respond and execute faster - accelerating decisions, customer support, and deployment of logistics use cases. On cost, Shipsy Brain uses fine-tuned, self-hosted open-source models instead of relying entirely on expensive frontier-model tokens. Specialized models require less computation and fewer tokens for logistics tasks, helping reduce the cost per interaction at enterprise scale. This approach also gives customers greater predictability and control, avoiding exposure to price increases or the discontinuation of lower-cost models by external providers. Against frontier models, a logistics tuned model wins on the documents that matter. | Metric | Shipsy Brain | Gem 3.5 | Gem 3 | Gem Pro | | Overall field extraction | 82.2% | 63.4% | 62.0% | 62.4% | | Logistics domain knowledge | 92.4% | 45.9% | 38.6% | 45.9% | | Document references | 83.6% | 51.6% | 52.8% | 44.1% | | Document understanding | 86.6% | 81.2% | 82.7% | 78.9% | The gap does not hold steady, it widens. Every shipment, decision and human correction feeds back into the layer,it learns from the correction, so the platform improves from what it runs.

Infocentric Solutions and Shipsy have partnered to bring AI-powered logistics solutions to enterprises in the Philippines. The collaboration combines Infocentric's expertise in systems integration and data governance with Shipsy's AI-native platform for orchestrating end-to-end logistics operations. The partnership aims to address challenges facing Philippine logistics teams, including legacy systems, fragmented distribution networks and rising last-mile costs. With the country's digital economy projected to reach $150 billion by 2030, demand for scalable logistics operations is growing rapidly. Shipsy recently launched AgentFleet, an AI workforce that automates operational tasks across customer experience and financial operations. The company serves over 250 customers in 30-plus countries and has been featured in Gartner's Magic Quadrant for Transportation and Warehouse Management.