
Pathmind
closedDeep learning for Enterprise on Hadoop and Spark.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor investor | €0.0 | round |
investor | €0.0 | round | |
investor investor | €0.0 | round | |
investor | €0.0 | round | |
investor investor investor | €0.0 | round | |
investor investor investor investor investor investor investor investor investor investor | €0.0 | round | |
investor investor investor | €0.0 | round | |
investor | €0.0 | round | |
$11.5m | Series A | ||
Total Funding | 000k |
USD | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|
Revenues | 0000 | 0000 | 0000 | 0000 |
% growth | - | 17 % | 53 % | - |
EBITDA | 0000 | 0000 | 0000 | 0000 |
Profit | 0000 | 0000 | 0000 | 0000 |
EV | 0000 | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 | 0000 |
Source: Dealroom estimates
Related Content
Pathmind, founded in 2014 by Chris Nicholson, developed a platform specializing in the application of deep reinforcement learning for industrial operations and supply chains. The company, which also operated under the name Skymind, was based in San Francisco and was a participant in the Winter 2016 Y Combinator batch. The firm's founder, Chris Nicholson, has a diverse background, having worked as a journalist for publications like The New York Times and Bloomberg News before transitioning into the tech world. This journey led him to roles at Y Combinator-backed startups before founding his own company, initially called Skymind, which later became Pathmind.
Pathmind's core business centered on providing a platform that used AI and simulation to enhance efficiency in complex industrial settings. The company catered to clients such as industrial engineers at large corporations and simulation consulting firms, operating in sectors like manufacturing, mining, logistics, and energy. Its technology allowed users to build digital twins or simulations of their physical operations—such as factories or warehouses—and then use deep reinforcement learning to identify optimal strategies for tasks like job scheduling, resource management, and vehicle routing. The platform was designed to automate the difficult aspects of applying AI, such as algorithm selection and hyperparameter tuning, enabling clients to focus on modeling their specific problems. By running countless iterations within these simulated environments, Pathmind's AI could learn the best operational decisions to maximize metrics like profitability and minimize inefficiencies like late deliveries.
The company's platform supported open-source frameworks and tools, including Python's Ray and RLlib, to train AI agents that could be deployed into real-world operational systems like ERPs. Before its eventual status as a dead-pooled company, Pathmind had successfully raised a total of $14.7 million in funding over six rounds. Its most significant funding was a Series A round of $11.5 million on November 21, 2018, led by TransLink Capital, with participation from investors like Y Combinator and ServiceNow.
Keywords: deep reinforcement learning, industrial operations, supply chain optimization, simulation software, digital twin, AI consulting, manufacturing AI, logistics optimization, Y Combinator, Chris Nicholson, Skymind, industrial engineering, machine learning, operational efficiency, predictive analytics, asset optimization, reinforcement learning platform, AI simulation, decision-making policies, process optimization