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

 Sept 01, Gurugram, India: Shipsy, an AI-native enterprise logistics management platform, 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 events recorded over five years.

●1 billion+ autoassigned decisions and 500 million+ human decisions, along 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 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

Accuracy:

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.

Shipsy Brain is currently available in beta for selected enterprises.