Do you have your own software products?

YES. We have developed solutions for many classical text analytics tasks, such as sentiment analysis, named entity recognition, language detection, topic modeling, etc. They are state-of-the-art, as we have shown in several international competitions. We can use our solutions to realize your project, but we also integrate third-party services and libraries, e.g. by Microsoft, IBM, or other service providers. The latest software we’ve developed is Interscriber, a tool for automatic transcriptions of audio files in several languages. We use a combination of 5 speech processing engines that consolidate the transcription results for better quality. We also offer the option to run exclusively on local servers to enforce data privacy and security. For more details, please visit the product website at http://www.interscriber.com

Can I get the source code?

YES. We usually hand-over the complete source code to our clients. You get all rights to modify and re-use it. This allows you, for instance, to re-train the machine learning models on your own if your data has changed at some time.

How can we integrate your solution in our infrastructure?

We always aim to deliver a software component with a very simple interface. For instance, our sentiment solution takes a plain text (e.g. a tweet) as input and outputs its sentiment (positive, negative, neutral) and some meta-data in a simple JSON format.
We usually use Docker to wrap our solutions, which makes it easy for you to deploy and scale it on your infrastructure.

Do you also do images, audio or other data types?

Our primary focus is on text analytics, where we are mostly working on. But our team has also experience in other domains, e.g. speaker detection, image classification or predictive analysis. In addition, we have a huge network of partners in both academia and industry, where we can usually find a suitable expert.

How good will the results be?

There are three aspects to consider:

  1. In general, the more time we invest, the better gets the solution. Every text analytics algorithm has several parameters that we can optimize, and tuning every step in the process usually improves the results.
  2. Every problem has an intrinsic complexity. For instance, the best solutions for sentiment analysis have an accuracy of approx. 73% (on tweets, 3 classes positive, negative, neutral), whereas named entity extraction can be solved with up to 93%.
  3. Your underlying data is important. Many tasks in text analytics are solved with machine learning, and these algorithms are trained and optimized on your data. The more data they have for training, the better the results.