THE WINNOW
Unlike most web scraping services that focus exclusively on the collection, Winnow provides a suite where the scrapers are only the tip of an intelligence tool that is specifically tailored for advanced analysis and customizability, designed (based on Human Centered Design principles) for ease of use and around the way our users already ask questions and make decisions, mirroring their thought process.
Collection > Rather than endless settings screens, Winnow’s suite allows the user to simply type in the name of a company or platform of interest, point the software at their website, known social media accounts, and other relevant sources on the web, and let the scraper go from there. In addition to the user-configured settings, our intelligent software also auto-discovers potential pages of interest and automatically adds them to the analysis pool. Finally, the scraped data enters a pre-processing step where ML-based models automatically append results of sentiment analysis, topic modeling, and other flags as crucial metadata.
Analysis > From there, our proprietary AI/ML algorithms sort, classify, and extract entities of interest automatically, making them available for natural language or traditional keyword searching and filtering.
Presentation > Any keyword or natural language search result can be turned into a dashboard element, gauge or graph, and certain limits or deviations from the norm can be set to trigger alarms automatically. At this point, dashboards can be saved, shared, or data exported for decision purposes.
Inclusiveness
We are gender intentional, aiming to seek and encourage the participation and engagement of female regulators, supervisors, and technologists in the implementation of our work, as well as incorporating gender equality / inclusiveness into the design of the suptech applications that we develop and in the conceptualization our data analytics projects.
Moreover, we are very intentional in designing and developing products that level the digital divides rather than deepening them, working to make technology more accessible for people with different physical and cognitive abilities, linguistic minorities, groups with low digital and financial literacy, etc. We also intend to drive fairness in AI systems by reducing algorithmic bias. We are aware that technology can exacerbate exclusion, therefore our explicit goal is to use data and tools for better inclusion outcomes.