Shipping AI: Powering the next gen of supply chains | Shipium

Too many shippers still rely on static data and inflexible configurations. This leaves them vulnerable to unexpected disruptions, missed opportunities, and subpar decision-making.

Shipium’s focus on real-time predictive accuracy enables you to design processes that account for what’s actually going on in the world. By using dynamic models that learn from each customer’s unique network and account for external events, you can go from understanding "what happened" to "what will happen."

Go from static to dynamic operations
Rather than relying on static criteria, we use dynamic machine learning models that continuously adapt to what’s happening in your network. Models learn from every shipment, enabling us to continuously improve outcomes and expand to new use cases our customers are focused on.

Predict anything, optimize everything
By modeling factors like real-world transit times and carrier capacity limits, we can predict the likely outcomes of process decisions and help you chart the best path forward.

Incorporate differentiated data
Our models are trained using external, real-time data sources. We account for things like weather, traffic, and carrier events to augment your data and create a more accurate picture of what's happening.

It's difficult to identify meaningful insights from massive datasets and even harder to confidently implement changes at the pace and scale required to create value.

That's why we're embedding AI as a co-pilot directly within our platform. Rather than getting bogged down in analysis, you get proactive, prescriptive recommendations that flag opportunities for savings and performance improvement that are impossible to discover manually.

Work with a co-pilot
We’re building a configuration copilot that proactively suggests optimal settings as you build carrier selection rules. It learns from successful customers, transforming a complex, manual setup process into a simple validation step. This ensures you're always aligned with best practices and set up to achieve your desired outcomes.

Get actionable recommendations
Our AI tools offer prescriptive insights that no single shipper could generate on their own. Our system analyzes your data to flag opportunities — from reducing late delivery rates to identifying cost-saving carrier options — moving you from passive analysis to active optimization.

One of the core benefits of AI is being able to accelerate tasks that are historically labor-intensive. This doesn’t just mean coordinating supply chain workflows — it also means simplifying technology management and helping to maximize the value of your investments faster.

At Shipium, we’re committed to using AI to make common tasks more efficient. This includes speeding up network configuration, simplifying integration, and eliminating the risks that result from manual processes.

Slash onboarding time
We’re using AI to quickly complete necessary onboarding tasks, such as creating accurate configurations for new origins. What was once a multi-week, carrier-dependent process now becomes a day-one capability, getting you up and running faster than ever before.

Streamline integration
Our team internally leverages an AI-powered mapping tool to automatically translate data from your host system into a format the Shipium API can consume. This drastically reduces complex data mapping and ensures faster, more scalable integrations with fewer errors.

Eliminate manual risk
We deploy ML models to automate the validation of carrier rates and surcharges. This directly addresses a significant source of manual effort and financial risk, ensuring your pricing is always accurate and up to date without the need for constant human intervention.

Today’s operators are stuck with technology that isn’t built for modern supply chains. Spending time analyzing why an order wasn’t shipped or manually adjusting to a carrier outage isn’t useful. From a business perspective, it’s a poor use of employees’ time.

That’s why we’re building AI agents to handle use cases that traditionally require manual intervention, but shouldn’t.

Engage with agents
We’re working toward implementing a natural language interface that allows you to ask complex questions and get clear answers, fast. For example, rather than running a time-intensive manual report, you can simply ask an AI agent, "Simulate the cost impact of switching 20% of my volume from Carrier A to Carrier B."

Identify and automate changes
Agents run autonomously in the background to identify and execute changes. With your pre-defined goals — like "minimize cost" or "maximize on-time delivery" — these agents will gradually shift from suggesting changes to autonomously implementing them, creating a truly self-optimizing system.