Supercharge Your Text Analytics With Qrvey

Your company generates data of all different shapes and sizes, but plain old text is still the most prevalent and surprisingly, still among the most useful and exciting. That’s because using the power of the cloud, along with the latest in machine learning, analyzing your text data holds limitless possibilities.

Qrvey was built with an all data accepted philosophy which is the first step in our text analytics journey. Qrvey’s Data Router makes it easy to connect to all of your company’s existing databases. It also quickly ingests text documents and audio files to ensure that all of your company’s text data can be analyzed. Qrvey even has the ability to collect new text data and augment existing text data using its built-in web form capabilities.

Once all of your text data has been connected, it is loaded into our high performance Elasticsearch analytics store. Elasticsearch is a key component of our analytics solution on AWS. Then the analysis can begin. Qrvey uses industry-leading text recognition and transcription services, like AWS Rekognition, to turn those documents and audio files into plain text that can then be used for analysis. All text data is then profiled through several other advanced AWS services to provide a host of additional useful metadata, including keywords, key phrases, names, places, and entities, all of which can also be used for analysis. Sentiment is determined at this time as weel, further enhancing the usefulness of your text data.

The final step in the text analysis journey at Qrvey is automation. Qrvey’s automation engine continually monitors your data looking for user-defined key metrics and thresholds. Once these workflows are triggered, Qrvey can take numerous actions automatically. These actions include writing data to third-party systems, calling webhooks or sending alerts and notifications.

There are numerous applications for text-based analysis. Call centers and support teams utilize Qrvey to analyze call recordings for sentiment and significant keywords. Automation then alerts managers to specific issues while also providing realtime dashboards and metrics to executives. When anomalies are detected, actions are taken automatically to update third-party systems and log the events.

Legal firms also use Qrvey to turn their mountains of discovery documents into searchable repositories. They use Qrvey’s data profiling to greatly enhance their research capabilities.

Qrvey’s data collection and transformation capabilities make it perfect for user feedback systems for SaaS applications. Text analysis is just one component of our platform’s overall capabilities.

David Abramson

David is the Chief Technology Officer at Qrvey, the leading provider of embedded analytics software for B2B SaaS companies. With extensive experience in software development and a passion for innovation, David plays a pivotal role in helping companies successfully transition from traditional reporting features to highly customizable analytics experiences that delight SaaS end-users.

Drawing from his deep technical expertise and industry insights, David leads Qrvey’s engineering team in developing cutting-edge analytics solutions that empower product teams to seamlessly integrate robust data visualizations and interactive dashboards into their applications. His commitment to staying ahead of the curve ensures that Qrvey’s platform continuously evolves to meet the ever-changing needs of the SaaS industry.

David shares his wealth of knowledge and best practices on topics related to embedded analytics, data visualization, and the technical considerations involved in building data-driven SaaS products.