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In 2017, Manan Shah and Shanif Dhanani, the co-founders ofApteo, came together under a common belief that technology and machine learning could improve investing. They created the company with the idea that they could use the latest techniques in A.I. and machine learning to replicate the workflow of a Wall Street Street analyst. Subsequently, they built a large A.I. engine that took in millions of data points from thousands of different sources, and used it to provide stock rankings through a B2C website named Milton. As they started to sell the data from Milton, they realized that finance professionals were facing their own problems in sourcing, managing, and analyzing data, so they pivoted the company and began building OneData, a data science platform for everyone.
As of January 2020, Apteo employs 11 people, many of whom have experience working at notable companies such as Twitter, DataDog, Thinknum, and Point72.
We provide a platform that allows users to visualize, forecast, correlate, and statistically analyze their data.
The platform allows users to correlate any any structured and quantitative dataset that's connected to the platform, including data that's structured as a time series.
The forecaster is a no-code, point-and-click interface that allows domain experts to use machine learning to create forecasted metrics for the future. Domain experts can easily set up a forecast by telling the system which metrics to forecast, along with any relevant data that they want the system to use, and can then receive a forecasted value of the relevant metric.
The forecaster uses machine learning to create predicted future values for key metrics. Users select the key metric that they would like to predict, along with any relevant datasets they think the system should take into account. The system then uses those suggested datasets, along with historical information about the metric, to learn any useful patterns for predicting future values of that dataset, by using that data to train various machine learning models. Once these models have been trained, the one with the best accuracy is selected and used to make a future forecast.
We incorporate standard machine learning metrics, including linear regression models, support vector machines, gradient boosting machines, random forests, and neural networks.
We have several different data connectors that allow us to plug in to the most common data sources, allowing us to integrate data into our platform wherever it’s stored. We continue to add new data connectors and accept requests for new connectors from customers.
We support many of the common data sources available today, including, but not limited to SQL and NoSQL databases, flat files, S3 buckets, APIs, data lakes and data warehouses. Additionally, we work with clients to support other data sources we don’t currently support.
We support many of the most common data formats and will work with clients to support any other formats they require.