Analyst Platform

With BigML, unleash PreSeries’ data and predict the future of startups and bring a fully data-driven approach to early-stage investing

The PreSeries Analyst Platform is a full version the BigML Machine Learning solution loaded with PreSeries’ own datasets and predictive models. BigML is a consumable, programmable, and scalable Machine Learning platform that helps organizations make highly automated, data-driven decisions by turning their data into actionable insights and deploying smart applications based on those.

Ready-to-use Datasets

Our datasets are-ready-to-use and fully customizable. Currently, we offer data on 300,000+ companies, 145,000+ rounds of financing, 6,000+ IPOs, 20,000+ acquisitions, and 6,000+ closed companies to compare.

Ready-to-use Models

Our machine learning models are entirely accessible and fully customizable. Want to build your own model to predict the chances of IPO for e-commerce startups in Brazil? Sure you can! Our baseline models can be used as stepping stones to derive your very own predictions. A wide range of modeling techniques are available: ensembles, clusters, logistic regression, anomaly detection, association discovery, topic models, and much more. And because we value privacy, our data, algorithms, and predictions are entirely restricted to you and your team.


With our RESTful API you can access all the functionalities of the the Analyst Platform from the command line. GUIs need not apply. Enrich your applications and data analysis workflow without having to step foot on the platform. Painlessly efficient, our API will improve and reduce time spent on your analyses . By combining our data and machine learning models with your data and models, you now possess the tools to stay ahead of the curve and have a better data-driven glimpse into the future of startups.

API Documentation

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BigML’s unmatched ease of use, powerful algorithms and auto-scaling Machine Learning optimized infrastructure, tens of thousands analysts, software developers, scientists, and students worldwide boost their productivity in solving real-life use cases by compositionally utilizing Classification, Regression, Cluster Analysis, Anomaly Detection, Association Discovery, and Topic Modelling tasks.

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