Alliance Trucking is a regional trucking company that’s been serving the Dallas-Fort Worth area for over 30 years. Alliance acts as a broker between truck owner-operators and construction companies who need loads of construction material delivered.
To request pricing for materials, Alliance’s customers email requests for quotes (RFQs) describing the location, the materials that need to be transported, and other details pertinent to the job. Alliance’s team then analyzes the emails to estimate trucking routes, availability, and costs and return a quote to the customer.
Running into friction scaling their core operations
Producing accurate quotes keeps Alliance competitive while ensuring a sound operating margin. But estimating the cost of a delivery is a complex human process that represents a lot of overhead. An estimator receives a bid in their help desk software, HelpScout. The bids come in as unstructured text with the details, including a schema of the location, material, and time frame. Estimators take that information, do the route research, and then manually input the job into Alliance’s purpose-built software.
This manual process created variation in their pricing, inefficient routes, and constrained the number of RFQs Alliance could respond to. Alliance wanted to scale their operations by offloading the route estimation to machine learning.
Using LightTag to build a dataset
Alliance needed to be able to feed emails directly into an NLP model that could understand RFQs and produce precise estimation criteria. The estimates would then be loaded into their software to be approved and executed. To build an NLP model for the trucking industry, they first needed to build a high quality dataset of job estimates from which their custom model could learn.
To ensure data quality, the labeling job fell to their CFO, Eric Dance. To maximize the return on investment he invested in labeling data, Alliance set out to find a solution that would make labeling simple and efficient and selected LightTag.
LightTag learned from Eric as he annotated and then provided pre-annotations that automated a large fraction of Eric’s work. An intuitive user interface made the software easy for Eric to use and build a dataset he was happy with, despite the fact that he is not a technical user.
“I’m not a developer and I don’t have those skills, but I understand my business and wanted to digitize it. I found LightTag easy to use to build a dataset of thousands of emails so we could get a quality dataset.”
-Eric Dance, CFO, Alliance Trucking
Eric was able to build a dataset with tens of thousands of annotations and train a precise RFQ estimation model. From there, Alliance automated the ingestion of bid emails and responses to RFQs in a faster, more consistent manner. Today, RFQs can be created and approved by a truck dispatcher in seconds, rather than minutes.
Alliance is a great example of how NLP can be applied not just to cutting-edge use cases, but to any business process. And it’s a shining example of how LightTag can dramatically improve the labeling process for subject matter experts to spend as little time labeling as possible, while still building a quality dataset to train a machine for human-quality results.
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