We found working with the Ango team professional, progressive, and scalable. We appreciate how the Ango team proactively seeks feedback and holds themselves accountable. Their QA team is careful, quick, and responsive, and their high-quality work has led to impactful and timely results.Saman Nouranian, Director of AI Research at WEIR Motion Metrics
WEIR Motion Metrics is a leading Canadian AI company, dedicated to help mining companies get the most out of their mining operations through the use of computer vision, machine learning models, and AI.
WEIR Motion Metrics receives, from its customers, terabytes of data each week. Before meeting Ango AI, they were annotating all of this data in-house. They were able to get high quality labels, but slowly, since managing this team became a massive overhead on their operations. The team could not be scaled quickly and it became a large expense for them.
WEIR Motion Metrics contacted Ango AI in the summer of 2021, expressing the need to outsource their labeling tasks. We quickly learned how the data needed to be labeled and scaled our team to accommodate their needs. Since then, we have been annotating data for WEIR Motion Metrics, combining a higher speed, lower cost, and a higher quality than previous labeling contractors WEIR Motion Metrics attempted to work with in the past. They also said our review and delivery processes are considerably faster than others. WEIR Motion Metrics has expressed the wish to continue working with Ango AI for the foreseeable future.
WEIR Motion Metrics is a leading AI company based in Vancouver, Canada. They are a B2B business focused on helping mining companies get the most out of their mining operations with the use of machine learning, computer vision, and AI.
They install cameras on their customers’ mining equipment, then process the images and videos they get to train their machine learning models. They then use these models to provide their customers with advanced AI-powered insights on their mining operations, such as statistics, real-time alerts, and more.
Before Ango AI
Machine learning models need to be trained before being used. In other words, if we want a model to recognize cars, we first need to feed it thousands of pictures in which the car is highlighted (e.g.: labeled.)
In WEIR Motion Metrics’ case, since ML models are their main product, data labeling is at the core of what they do. Each week they receive terabytes of data from the cameras installed on their customers’ quarries, and a good chunk of this data needs to be labeled and fed to the model for training.
Before Ango AI, they annotated all of this data internally, using a data labeling team of their creation. This led to a number of downsides which will be elaborated below.
Lack of flexibility
Occasionally, WEIR Motion Metrics would need to label more data than usual. This variation in quantity means that they would need to constantly grow their team when there is more data, then scale it back when they have less data. This process is not easily feasible for a company that does not focus solely on data labeling.
Hiring new data labeling talent is expensive, both in time and resources. And even when hired and in the team, there will be times when there is less data to label. During those times, the new team members will be paid yet with no tasks to complete as there is no data to process.
In short, WEIR Motion Metrics was stuck between two negative situations: either labeling all data as it comes, but leading to wasted human labor, or optimally utilizing the team, but without being able to label all data in time.
WEIR Motion Metrics is, at its core, an AI company. AI is what they do best, and it is the core of their business. Having to create, then manage an internal data annotation team is an extra overhead, bringing unnecessary complexity and deviating the team away from their main occupation.
Having an in-house labeling team means having to manage each individual, involving HR and other departments in the process. Often, for higher-security scenarios, this means finding office space and thus providing various benefits to the annotators. Extra personnel needs to be hired simply to manage the team, let alone the team itself.
Having a team in-house means having to reinvent the wheel each time. While labeling data in-house appears as if it is the easiest solution upfront, when the team grows, so does the complexity of managing it.
For example, they had to develop their own data labeling software, since open-source, off-the-shelf solutions did not work for them. This is an entire new product they had to now maintain, update, and manage.
Other complexities of doing data labeling in-house for WEIR Motion Metrics were to make sure that every labeler was on the same page and received clear instructions, that their performance was accurately measured, and more.
It comes as no surprise that hiring a dedicated data labeling team, creating a new data labeling platform, hiring people to manage such a team, as well as renting office space and purchasing equipment comes at a high cost.
After Ango AI
WEIR Motion Metrics contacted Ango AI in the summer of 2021. Realizing the costs, complexity, and inflexibility of doing labeling in-house, they expressed the wish to outsource their labeling tasks to a third party.
We quickly understood their needs and stepped in to help. We created channels of communications and met up often at the beginning of the project to establish exactly how the project was going to move forward, what they expected from the annotations, and what we could provide.
As soon as we received the data from WEIR Motion Metrics we started annotating it, and this brought several advantages to our customer.
Flexibility and Speed
Now, whenever our customer needs to label more data than usual, they simply send it to us and we take care of everything. Thanks to our flexible labeling team and hiring pipeline, we are able to scale up and down our workforce extremely quickly to deal with spikes in customer data.
This also means that when WEIR Motion Metrics has less data to label, they only need to pay for the data that is actually being labeled instead of maintaining a team of annotators in standby. If they send us less data than usual, we simply reroute our workforce to another one of our projects, resulting in a win-win for both of us.
And when the customer needs more speed, they can simply tell us and we will dedicate more annotators to their task, with no upper limit to how fast we can go. WEIR Motion Metrics has explicitly expressed that our review and delivery processes are faster than with any other company they tried before.
The head of WEIR Motion Metrics’ labeling team has said that Ango AI managed to match and exceed the quality of the labels that were produced by labeling firms they tried to work with before. This is likely because of a variety of reasons, and some are the following:
Quality-Centric Labeling Platform
Our software team is highly experienced in crafting solutions for high quality data labeling, and our software is packed with features to increase the quality of the labels: features we have added over time as we learned from all of our previous experiences. WEIR Motion Metrics are, at their core, not a labeling software company, and do not have previous experience with labeling. Thus, we were able to provide them with a high quality platform which helped in keeping label quality high.
Highly Trained Annotators
Our annotators are highly selected and trained. They can quickly adapt to new labeling tasks, and they immediately understood how WEIR Motion Metrics needed their data. This led to labeling quality to quickly get to the level required.
Since choosing Ango AI, the customer does not need to maintain an internal data labeling team anymore, saving significantly in both time and cost. While outsourcing does have a cost, we and other AI research companies estimate that the cost of outsourcing is about 1/5th the cost of doing data labeling in-house.
The overhead of performing data labeling in-house was completely eliminated. Instead of having to create an internal pipeline for labeling, all they do now is send us their data and get their labels back completely done. This way, they can focus on their main business proposition of creating world-leading machine learning models instead of dedicating time and resources to data labeling.
- Ango AI has matched and exceeded the quality of all other labeling companies tried by WEIR Motion Metrics in the past.
- WEIR Motion Metrics has stated that Ango AI’s review and delivery processes are faster than any other company they worked with in the past.
- Ango AI has been able to complete and deliver labels at a speed and quality that was at least on par with their in-house results.
- Ango AI has allowed the customer to save significantly on their data labeling efforts. While we do not have a precise number, AI analytics firms estimate that the savings can be up to 80%.
- The customer has stated that Ango Hub saved them time and resources, that they are happy with the service, and that they intend to continue with Ango Hub in the future.