Greenscreens.ai is a dynamic pricing engine for freight brokers that uses AI and machine learning to provide predictions of real-time truckload Buy Rates customized to an individual freight brokerage's buying power.

A buy rate is the cost of a truck in a given lane at the time of the prediction.

The Target Buy Rate is the rate that the model predicts will be the best option for a customer when they enter a lane and transport type into our UI. Different brokerages will usually have different predicted rates on the same lane due to their difference in buying power as derived from their historical data. This rate is primarily based on the customer’s historical data, but can also be influenced by external data, such as other brokerages that contribute data to the network or external macroeconomic factors. The Target Buy Rate also takes the day of the week into account, as prices tend to run higher for weekend pickups.

The Greenscreens.ai target rate is AI generated using a data training set to train a unique, tailored model composed of and heavily dependent upon the data and buying power of a specific brokerage in conjunction with market conditions and Greenscreens.ai network data.

The Target Buy rate does include fuel. The predicted fuel cost included in the Target Buy Rate is based on data for the lane from your brokerage and the Greenscreens Network. Because Greenscreens calculates rates based on historical data for the entire linehaul, the amount allotted for fuel in the Target Buy Rate will differ from the market average listed under Fuel, which is there for the sake of comparison and for offering a cost breakdown to shippers.

The Start Rate is a minimum predicted rate for which the load could potentially be booked and is our suggested opening offer for carriers based on current market conditions.

When the machine learning model predicts a Target Buy Rate, it also predicts maximum and minimum possible rates, with the Target Buy Rate somewhere in the middle. The Target Buy Rate is the model’s prediction for the most likely rate on the load. The Start Rate is the prediction for the lowest possible successful rate.

The algorithm deduces the Start Rate by evaluating the potential variability of the load price around the Target Buy Rate. It rounds the lowest number predicted as a possibility down to the nearest $50 to get the Start Rate. Where the Confidence Level is lower, you’ll see a lower Start Rate. The Start Rate helps guide price discovery where the Confidence Level is on the low side.

The Greenscreens Network Rate represents the expected rate in the market. The Network Rate has no bias towards any one customer’s data. It can be lower or higher than the target rate, depending on your brokerage’s buying power vs the general buying power of the market. The Network Rate also takes the day of the week into account, as prices tend to run a little higher for weekend pickups.

The Target Buy Rate is the rate that Greenscreens predicts will succeed for your particular brokerage. It reflects both market conditions and your brokerage’s specific buying power. The Network Rate is the predicted market rate. It’s based on data from the Greenscreens Network and is not biased toward any one brokerage.

The Target Sell Rate is the quote the machine learning model predicts as most likely to win the customer. The rate is based on the brokerage's previous quotes for the lane and customer. The brokerage can also set a specific markup for a particular customer, and the model will use that instead. If no historical data is available and no markup rules apply, the markup will default to 15%.

The Confidence Level represents the ease of successfully booking a truck for that rate. It usually falls between 50 and 100. This number does not necessarily represent a more or less accurate prediction. The higher the Confidence score, the easier it should be to book at the predicted rate.

The Better Rate tells you which rate between the GS Network Rate and Target Buy Rate has a higher Confidence Level indicating which rate might be quicker to cover and easier to find capacity for based on current market conditions.

Similar Lanes share similar features, such as a common destination. A Greenscreens machine learning model recognizes similarities and connections between lane features, which helps it to make accurate predictions, even for new lanes. This is especially useful when making predictions for lanes with little or no historical data.

Some Greenscreens widgets include a toggle at the top right with the options Lane, 3d ZIP, and Mkt. This lets you view data for the wider area surrounding the lane in the prediction. A green dot next to one of these options means that data is available.

The Top Carriers listed for the shipment are carriers from the brokerage's carrier network who have moved the load for the brokerage over the past 60 days. The Top Carriers section includes details about volume, price point, how recent the shipment was, and contact information. It's there to help you choose the best carrier and connect with them easily.

The Negotiation Coach helps you negotiate with customers by offering a quick summary of conditions that might affect rates, such as current events, load history, market dynamics, and holidays.

Greenscreens.ai machine learning models depend heavily on historical load data to assist in rate generation. While there is alternative market data that helps paint a forecast, the model performs and corrects best when analyzing historical load data, which gives it a foundation for how rates should be compiled and helps the model improve.

The Network Rate is based on data from brokerages in the Greenscreens.ai network. When a brokerage requests a rate, Greenscreens draws on data from over 100 customers with $18 billion in annual truckload revenue to provide this rate and accompanying insights into pricing trends in the same market area.

The Confidence Level is primarily determined by three things:

Before making a new record of data available to test and training sets, we run the record through a filtration process that identifies whether the given record has surpassed certain thresholds. These thresholds are designed to protect the model’s integrity from data that might skew predictions.

The Greenscreens AI considers numerous market conditions and trends, such as seasonal change and load characteristics. These are used daily to train the AI, which makes adjustments to the prediction algorithms across every lane Greenscreens.ai supports. Predictions can be noticeably lower/higher than the historical average at times, due to the nature of the lane and where the data may be pointing next.

Artificial Intelligence is the general ability to emulate human thought and behavior through computer science, while machine learning refers to the specific technologies and algorithms that enable systems to identify patterns, make decisions, and improve their capabilities through experience and data.

Machine learning is a field within artificial intelligence. It deals with the process behind systems that use inferences and statistical models to find connections in data. Patterns in the data allow the model to draw conclusions and make predictions.

Machine learning training is the process of building and calibrating a model's logic based on historical data to help it answer questions correctly. In most cases, the machine “learns” by testing examples and receiving feedback on how close each result is to being correct. It continues to adjust the “weight” it assigns to each factor in determining a result, and over time, the model gets better and better at predicting outcomes, while following new trends appearing in more recent data.