Freight Optimisation Workshop
Struggling with complex freight needs, multiple carriers, or rising costs?
In a two-stage process, we’ll deep-dive into your freight setup and provide tailored solutions.
No pressure, just valuable insights to transform your freight management.
How quickly has AI become mainstream; hidden in your favourites apps, quietly working behind the scenes to personalise your experiences and creating content that is remarkably ‘human’. At work too, it’s likely woven into the systems you use every day streamlining your processes to help you work more efficiently.
This is exactly why we’ve developed Cario AutoPilot - to drive significant improvements in our customer’s efficiency and accuracy when moving their products from A to B.
Before we get into specific capabilities, we wanted to share a little about our AI journey.
To function well, AI needs data. Lots of it. And in freight management, we’re flush with it. We knew that if we could mine the data-rich environment that is often siloed in freight management solutions, we could extract critical information and insights that would allow our customers to navigate the complexities of their freight with a newfound confidence.
Beyond needing to capture the vast data reservoir, we wanted to achieve two outcomes with Cario AutoPilot:
- Proactively identify issues within the network in real-time and resolve them pre-emptively.
- Reactively address these issues to ensure customers receive quicker responses.
For example, if we detect that freight isn't scanning at the correct milestone to meet its estimated time of arrival (ETA), then Cario AutoPilot would need to be able to automatically raise a query to the carrier and assign a customer service ticket to our internal team. Any updates in data and responses would then be seamlessly integrated back into Cario.
Or, when a customer enquiry comes in, AI must be able to read the enquiry and determine if it can resolve it autonomously. If possible, it would then retrieve the relevant data (e.g., proof of delivery) from Cario (the platform Freight People is built on) without human intervention. If not, it would fetch all necessary information from various source systems, enabling our customer service team to resolve the issue promptly.
So, to capture the vast data reservoir within ours, and our carrier systems, we developed Cario AutoPilot.
The Evolution of Cario AutoPilot
Cario AutoPilot has undergone significant change throughout its five versions, each iteration building upon the last to deliver on our functionality and efficiency goals.
Stage 1 - Connote Detection
Our Drivers:
- Minimise both time spent and the risk of manual data entry errors associated with managing consignment notes
- Driver accuracy when handling shipments
We wanted Cario AutoPilot to be able to automatically identify and extract information from consignment notes (connotes), eliminating a large chunk of repetition and data duplication.
We accomplished this by integrating optical character recognition (OCR) technology into the platform. Following thorough development and testing, Cario AutoPilot can now scan and interpret the text on every consignment note, extracting key data such as sender and recipient information, shipment contents, tracking numbers, and delivery instructions.
Stage 2 – Automated Responses
Our Drivers:
- Speed up and improve communication
- Enhance customer satisfaction
- Streamline workflows
Next on our agenda was a plan for Cario AutoPilot to generate pre-defined and human-like responses to inquiries, requests, or actions. These could be initiated either by our customers or freight carriers.
Using AI, we successfully engineered the platform to automatically respond when triggered by specific events or conditions. This functionality covers notifications, acknowledgments, status updates, or confirmations related to shipment bookings, tracking inquiries, delivery updates, and other interactions.
Stage 3 – Understanding and Separating Ticket Types
Our Drivers:
- Provide prompt and appropriate responses to customer inquiries, service requests, or operational issues.
This stage was all about improving the system's ability to categorise and distinguish between different types of tickets or requests. To achieve this, we employed sophisticated algorithms and machine learning techniques to analyse and interpret incoming tickets based on their content, context, or metadata.
By the end of this phase, we had successfully trained the algorithms to accurately categorise tickets so the system could prioritise, route, and handle them more effectively.
Stage 4 – Improving Response Accuracy
Our Drivers:
- Improve customer experiences
- Streamline processes
- Build trust in the platform
This stage was centred on fine tuning capabilities to improve the precision and correctness of the automated responses generated by the system in response to customer inquiries or actions. Our team got to work refining the algorithms, machine learning models and rule-based systems used to generate these responses. They also improved the underlying data processing and analysis techniques to reduce any errors and inaccuracies in the responses provided to customers and carriers.
Stage 5 – Efficient Integration
Our Drivers:
- Improve efficiencies and customer service
- Promoting collaboration between teams and systems
This final stage involved establishing an integration between the Freshdesk customer support platform and Cario AutoPilot to support a smooth flow of data between the two systems.
A seamless integration has allowed customer inquiries and tickets from Freshdesk to be automatically synced with relevant shipment or order data. Simultaneously, updates or actions taken in Cario - such as shipment status changes - are reflected in Freshdesk to ensure a cohesive user experience across both platforms.
Connecting data from your Business Critical Systems
In order for Cario AutoPilot to work, it needed to be able to talk with other freight-specific systems. So we have – and continue to – build these integrations with the third party solutions most used by our customers. Here are some of the systems that we have integrated.
For PoD and ETA Extraction:
- TGE, Border and Couriers Please – integrated and live
- Hi-Trans – coming soon
For Proactive Exception Reporting to automate the monitoring of consignments and trigger actions for any unusual delays/exceptions or missing PODs:
- Enabled and live for many customers.
For Automated Extraction for ETAs, Time Slot Booking and Events Data:
- Live for many carriers across all customers namely for Border, TeamGE, VTFE, Allied, TNT, Aramex, Direct Freight Express, etc.
Automated Generation of Live Daily Reports for various Customers with comments
How does Cario AutoPilot help you?
One of Freight People’s greatest super powers is that we transform the way organisations work with freight, turning it into a competitive differentiator for your business. Cario AutoPilot and all its new AI, OCR and new generative AI smarts is just another way we’re helping do just that.
These secure, trustworthy and intuitive AI experiences are working behind the scenes now, and each new feature simply works in the background to optimise your entire end-to-end freight process to save your team time and money.
If you would like to learn more about these innovations, feel free to reach out to our team. We will also be keeping you updated as we add new features and capabilities to our tech-rich freight services.
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