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Questions?

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What does integration look like? How long does it take to show value?

Integration is actually very simple. What usually takes most time is to correctly define the input (images, fields, etc) and output (schema) as well as write detailed instructions (please refer here to how to write instructions). Once this is done, implementation is quick: connect to our API or download processed data in batches from the dashboard.

What are the SLAs you typically agree to?

Most of our clients need their data processed within 24 hours. For some clients, we can return data within 2 hours and once our AI is trained, it's instant. The exact SLA will depend on your desired levels of quality, cost and speed.

What is the ROI?

On average, we have save achieved cost savings of about 62% for our customers. Results vary per use case. To understand what we can do for your business, think about how any tedious, repetitive work you have your employees perform either on a permanent or on a temporary basis. We can probably automate 70% in the near term, and up to 99% in the long term

How does this compare to Google Cloud AI and Amazon Mechanical Turk?

Google Cloud AI and MTurk are like their VM/servers: extremely powerful and require a lot of set up and configuration with little support. Using our solution you get a guarantee of quality (our labelers both pass a qualifying exam and are monitored throughout the labeling process and disqualified if their performance drops), a customer success agent to help you write and iterate on your instructions and analyze your results, and built-in machine-learning-based automation of labels where applicable.It’s the difference between a fully managed assembly line that works immediately and an empty factory with a lot of potential. Of course, we build on top of those powerful building blocks. Set up a call to discuss more.

So far no one is able to match the quality I get internally. How do you do it?

We use a set of ground truth data to measure the quality of each method of labeling.  We do this by applying 12 different kinds of checks that we apply against each source and each method of labelling, be it machine or humans. We are then calibrating our router according to the quality score of each source of labeling and will route your tasks to the best worker. This allows us to 'weed out' poor performance and optimize the process for you. You can set performance targets for the quality (as well as cost and latency) you need and we will guarantee the performance accordingly. Make sure to set the performance target according to your business needs: better quality may lead to higher initial costs. To guarantee high performance from the start, we are also training our crowd analysts. For a group of tasks, e.g. OCR/image transcription, crowd analysts have to pass qualifiers/exams, and only qualified analysts are allowed to perform certain tasks. For larger projects, we work with our clients to write custom qualifiers before we even perform the first tasks.