
Ople
World class deep-learning in days, not months.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor investor | €0.0 | round |
N/A | €0.0 | round | |
investor investor | €0.0 | round | |
N/A | N/A | Series A | |
Total Funding | 000k |
USD | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|---|
Revenues | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
% growth | - | 30 % | - | (12 %) | 22 % | - |
EBITDA | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
Profit | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
EV | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
Source: Dealroom estimates, Company filings or news article
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Ople.ai operated as an artificial intelligence software company with a mission to simplify and accelerate the adoption of machine learning for businesses. Founded in 2017 by Pedro Alves and headquartered in Silicon Valley, the company developed a platform that automated many complex data science processes. The firm's core offering was a software-as-a-service (SaaS) platform, accessible via Amazon Web Services (AWS), designed to empower business users, data analysts, and citizen data scientists—not just PhD-level experts—to build and deploy production-grade predictive models.
The inspiration for Ople came from founder Pedro Alves's extensive background as a data science and machine learning expert. Having led data science teams at companies like Microsoft and Banjo, and with academic work in computational biology at Yale, Alves recognized a critical bottleneck in the industry. He observed that data scientists spent significant time on repetitive tasks that could be automated, which limited their ability to focus on high-impact business problems. This led to the core concept behind Ople: using AI to build AI, a process the company termed "Behavioral Assimilation." The platform was designed to learn from every model it built, progressively getting faster and smarter, mimicking how a human data scientist gains experience over time.
The platform's business model centered on providing this automated machine learning (AutoML) solution to enterprises, aiming to make AI cheaper, easier, and more ubiquitous. It allowed companies to move from raw data to actionable predictions in days or even minutes, a process that traditionally took months. The technology was engineered to integrate with existing business intelligence tools like Tableau and Google Sheets, enabling users to embed predictive analytics directly into their workflows without needing deep technical expertise. Ople targeted a broad market, from business leaders seeking strategic advantages to data professionals aiming for greater efficiency. The company successfully raised a total of $25.1 million over several funding rounds, including a Series A and a Series B, from investors such as Triage Ventures, Hack VC, and Plug and Play Tech Center. In October 2021, Ople.ai was acquired by Aktana, a company focused on intelligent customer engagement for the life sciences industry.
Keywords: automated machine learning, AutoML, predictive analytics, data science automation, AI platform, machine learning models, Pedro Alves, citizen data scientist, AI for business, enterprise AI, predictive modeling, Behavioral Assimilation, cloud AI platform, deep learning solutions, AI model deployment, Aktana acquisition, data science efficiency