Gurugram-based logistics technology company Shipsy has launched the beta version of Shipsy Brain, a logistics-focused artificial intelligence (AI) intelligence layer designed to help enterprises make and execute operational decisions with greater speed, accuracy and control. The product is integrated into Shipsy’s AgentFleet platform.
Shipsy Brain combines specialised open-source AI models with logistics data generated through the company’s platform. The system is designed to understand the operational context around shipments, drivers, documents, carriers, contracts and other supply-chain variables—areas where general-purpose AI models may lack industry-specific context.
Built on large-scale logistics data
Shipsy said Shipsy Brain draws on more than five years of operational data, including:
- More than 50 billion operational events
- Over 1 billion auto-assigned decisions and 500 million human decisions
- More than 100 billion GPS location pings
- Over 3 billion delivery labels
- 342 carrier integrations and more than 50 million routing decisions
- Around 3 billion hub scans
- More than 5,000 logistics workflows
This data gives the AI system a detailed understanding of logistics-specific terminology, processes and operational patterns.
AI agents move from recommendations to action
Shipsy Brain acts as a central intelligence layer coordinating specialised models covering documents, consignments, trips, workflows and finance. These models can support AI agents performing tasks such as document validation, address intelligence, anomaly detection, ETA prediction, route optimisation, settlement management and workflow recommendations.
Within AgentFleet, the system monitors live logistics operations, identifies tasks that can potentially be automated and, where appropriate, seeks permission before executing actions. Human corrections can also feed back into the system, allowing it to improve its future decisions.
Focus on accuracy, speed and cost
Shipsy said its logistics-specific approach is intended to improve AI performance across three areas—accuracy, speed and cost. The company uses specialised, fine-tuned models rather than relying exclusively on large general-purpose frontier models, which can reduce computing requirements and improve predictability of AI deployment costs.
The company also reported an 86.6% score in document-understanding benchmarks for its logistics-focused model, highlighting the potential advantage of domain-specific AI for complex logistics documentation and data extraction.
Model-agnostic architecture
A key feature of Shipsy Brain is its model-agnostic architecture. AI models connected to the intelligence layer can access the broader logistics context without requiring the underlying context infrastructure to be rebuilt each time a new model is introduced. This allows enterprises to incorporate improvements in AI models while retaining their logistics-specific operational knowledge.
The launch reflects the broader evolution of logistics technology from systems that primarily record and display information towards AI-enabled platforms capable of reasoning, recommending and executing actions within defined business controls.
Shipsy Brain is currently available in beta to selected enterprises, with the company positioning it as a foundation for more autonomous and intelligent logistics operations.