Shipsy, today announced the beta launch of Shipsy Brain, a logistics-native intelligence layer designed to help enterprises orchestrate decisions and execute operational workflows with greater accuracy, speed and control across 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 towards AI systems that can reason, recommend and act within clearly defined business controls,” said Soham Chokshi, Co-founder and CEO, Shipsy.
What sits behind Shipsy’s brain?
Shipsy Brain combines specialised open-source models with logistics data generated through Shipsy’s platform, providing AI agents with the operational context required to make decisions across complex supply-chain environments.
The intelligence layer draws on:
- 50 billion+ operational events recorded over five years.
- 1 billion+ auto-assigned decisions and 500 million+ human decisions, together with reassignment history.
- 100 billion+ GPS location pings connected with route and delivery outcomes.
- 3 billion+ delivery labels across different carriers and formats.
- 342 carrier integrations and more than 50 million routing decisions.
- 3 billion+ hub scans covering the movement of shipments through the network.
- 5,000+ workflows built across Shipsy’s Workflow Builder and AgentFlow platform.
This operational data enables Shipsy Brain to interpret logistics-specific context that may be ambiguous to general-purpose AI models.
Shipsy Brain is designed as a central intelligence layer coordinating specialised models across areas including 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.
The intelligence layer operates within Shipsy’s AgentFleet platform, where it monitors live operations, identifies manual activities that can be automated, requests permission where required and self-executes actions based on confidence levels. Human corrections can also feed back into the system, enabling the platform to learn from operational outcomes.
As a context layer, Shipsy Brain is model-agnostic. Models connected to it can access the broader operational context, allowing improvements in general AI models to be applied to logistics use cases without rebuilding the underlying context layer.
Accuracy, speed and cost at enterprise scale
Shipsy said the platform is designed to improve AI performance across three key areas: accuracy, speed and cost.
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, including different terminology and references used for consignment numbers, that general-purpose models may not recognise consistently.
In document-intelligence benchmarks, Shipsy Brain’s model achieved an overall document understanding score of 86.6%, compared with 81.2% for Gem 3.5, 82.7% for Gem 3 and 78.9% for Gem Pro.
The model recorded 92.4% for logistics domain knowledge, 83.6% for document references and 82.2% for overall field extraction.
At speed, Shipsy uses fine-tuned logistics-specific models that already understand industry terminology, workflows and operational context. This reduces the need for extensive prompts to establish context for individual tasks, potentially accelerating decisions, customer support and deployment of logistics use cases.
On cost, the platform uses fine-tuned, self-hosted open-source models rather than relying entirely on frontier-model tokens. Shipsy said specialised models require less computation and fewer tokens for logistics-specific tasks, helping provide greater predictability and control over AI costs at enterprise scale.
Moving from AI assistance to operational action
Shipsy Brain is positioned as an intelligence layer for the next stage of enterprise AI adoption, where AI systems move beyond dashboards and copilots towards reasoning and execution within defined business controls.
Its ability to combine logistics-specific data, specialised models and workflow context is designed to allow enterprise agents to make decisions based on the operational realities surrounding a shipment, rather than treating individual tasks in isolation.
The platform also creates a feedback loop in which human corrections and operational decisions can contribute to improving future performance.
Shipsy Brain is currently available in beta for selected enterprises.