Quality at scale is hard to obtain
You frequently run into this dilemma: how do I get human accuracy at AI speed? This balance is hard to strike. AI often has a low accuracy and human BPOs are not fast enough.
Data scientists have to do monotonous work
Your highly skilled and expensive team of data scientists has to perform highly repetitive and monotonous tasks, such as data labeling, instead of focusing on more meaningful tasks.
Your in-house tools and processes can't scale fast enough
Even when you have some in-house labeling capabilities, aggregating and scaling often takes way too long.
It's often a human or AI-based solution—not both
Most solutions out there only offer one of them, and you often have to spend time and money to find out this is not sufficient for your needs.
Speed and ease of integration often miss the mark
Getting your project live—from aggregating data and building the dataset to POC—can take several months.
Existing solutions are not made to order
Most solutions are designed to meet high level generic needs. They are not customized and flexible enough for your specific use case and speed of execution.