StageSeed VC | Alive
Last Raised$1M | 3 yrs ago
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Apteo's Products & Differentiators
Apteo's SaaS Product
Apteo's main product is an analytics solution powered by A.I. that automates segmentation and cross-sells. At our core, we analyze all of a brand's customers, figure out who their customers are, what they're likely to do next, and we plug in to their existing marketing systems to help them use those predictions to send personalized and targeted messaging. On top of that, our tool has a built-in, full-featured analytics and BI solution that provides deep insights into customer journeys, bundles, retention, and customer lifetime value.
Expert Collections containing Apteo
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
Apteo is included in 3 Expert Collections, including Capital Markets Tech.
Capital Markets Tech
Companies in the capital markets tech space are providing software and/or services for investment banks, hedge funds, investment managers, and so forth. Companies included use technology across the front, middle, and back-offices and streamline all pre- and post-trade operations.
This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.
Latest Apteo News
Sep 29, 2020
How Companies Can Gain Value From Small Data Big data is all the rage today, and rightfully so. State-of-the-art language models powered by big data, like GPT-3 , can write beautiful prose, create realistic news articles, translate text, write functional code in any language, and more. Further, state-of-the-art vision models trained on massive datasets are bringing us towards level 5—or fully autonomous— self driving cars . While big data can fuel astonishing results, organizations can gain value from “small data” as well. In this article, I’ll highlight four ways to circumvent the need for big data. 1. Exploratory Analysis Whether you’re working with big or small data, you should understand your data before you try to gain deep insights from it. This includes calculating simple descriptive statistics, like count, mean, quartiles, the minimum, the maximum, and so on. Slightly more complex analyses include histograms, scatterplots, pie charts, and so forth. Further, correlation analyses can be done to confirm or reject hypotheses about how the data is related. You’ll also want to analyze data quality, and deal with problems like missing data and outliers. Anything that helps you understand the data itself should be done at this stage. 2. Basic Machine Learning Models Machine learning is a lot more than just deep learning, and alternative techniques like decision trees are far simpler, more explainable, and more resource efficient, while working well with less data. Slightly more complex techniques, like Random Forest and Support Vector Machines, also work great on smaller datasets, while still being much easier to set up than neural networks. Highly complex techniques like deep learning shine for tasks like image classification and natural language processing. For these kinds of problems, having more data is almost always better. That being said, there are even ways to combine these approaches, such as with neural-backed decision trees , that offer high accuracy on tasks like image classification, while maintaining the relative simplicity and explainability of decision trees. 3.Transfer Learning Another method is transfer learning, which allows you to transfer the knowledge learned in one dataset and apply it to another dataset. As a result, you don’t have to start from scratch, and you can train machine learning models with far less data. For example, companies can currently beta test OpenAI’s GPT-3 model, which allows you to generate natural language of any kind, without needing to train on any data at all. This is an example of zero-shot learning. To increase the model’s accuracy for your specific use-case, you can train the model on a small amount of your own data, known as few-shot learning. In either case, the model is already trained on a corpus of almost the entire Internet’s text, and the learning is available for you to get an accurate language model out-of-the-box. For other tasks, like image classification, you can apply transfer learning using models like VGG16 or ResNet50. 4. AutoML Another method to quickly deploy AI, without needing big data, is by using turn-key automated machine learning solutions that are pre-trained on big datasets. Some products include Google Cloud’s AutoML, Salesforce Einstein AutoML, Microsoft Azure AI, and Amazon AutoGluon. With so many options to choose from, AutoML is a great way to implement AI in your organization, even if you don’t have big data. Conclusion It’s a common misconception that machine learning needs big data. Statisticians have been working with small data for decades, and techniques like exploratory analysis, classical machine learning, and AutoML are great ways to gain insights from any data set, no matter the size. About the Author Shanif Dhanani is a former Twitter data scientist and engineer turned CEO of Apteo . Apteo is a no-code analytics platform anyone can use in a matter of minutes to extract deep insights in their data. Sign up for the free insideBIGDATA newsletter .
Apteo Frequently Asked Questions (FAQ)
When was Apteo founded?
Apteo was founded in 2017.
Where is Apteo's headquarters?
Apteo's headquarters is located at 115 E 23rd St, New York.
What is Apteo's latest funding round?
Apteo's latest funding round is Seed VC.
How much did Apteo raise?
Apteo raised a total of $1.1M.
Who are the investors of Apteo?
Investors of Apteo include Ripple Ventures, Plug and Play Accelerator and Entrepreneurs Roundtable Accelerator.
Who are Apteo's competitors?
Competitors of Apteo include Nosto and 4 more.
What products does Apteo offer?
Apteo's products include Apteo's SaaS Product.
Who are Apteo's customers?
Customers of Apteo include Greensbury, Elizabeth Mott and Ecom Honey.
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