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§ Private Profile · Pleasanton, CA, USA
Edge AI software platform integrating DevOps, MLOps, and DataOps for real-time operational decision automation in industrial firms.
Founded in 2015 by Anthony Hill, Pleasanton, California-based Adapdix develops an edge AI and machine learning software platform designed to automate industrial processes and manage predictive maintenance. The company's core EdgeOps system integrates data management and analytics directly at the network edge, allowing semiconductor manufacturers and industrial enterprises to reduce equipment downtime and improve overall supply chain efficiency. Operating with a team of approximately 28 employees, the enterprise software provider generates an estimated $2,700,000 in annual revenue through its B2B licensing agreements. Adapdix has secured $8,400,000 in total venture funding, highlighted by an $8 million Series A financing round backed by institutional investors WRVI Capital, Micron Ventures, and Xilinx Ventures. The firm recently expanded its hybrid cloud data management capabilities through the strategic acquisition of Edge Intelligence and a commercial partnership with manufacturing AI provider BISTelligence.
ADAPDIX has raised $10.0M across 2 funding rounds.
ADAPDIX has raised $10.0M in total across 2 funding rounds.
ADAPDIX has raised $10.0M across 2 funding rounds. Most recently, it raised $2.0M Series U in February 2021.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Feb 1, 2021 | $2M Series U | — | BIP Capital | Announced |
| Dec 1, 2020 | $8M Series A | Nicholas Brathwaite | Celesta, Chris Kelly, Sasha Mirchandani, Andrew Byrnes, X2 Equity | Announced |
# High-Level Overview
Adapdix is an edge AI/ML optimization platform company that enables enterprises to deploy and manage artificial intelligence and machine learning capabilities at the edge of their networks.[1][2] Founded in 2015 and headquartered in Pleasanton, California, the company serves a growing portfolio of Fortune 500 customers by automating operational and business decisions in real-time while reducing equipment downtime, supply chain costs, and improving remote worker productivity.[2][3]
The company's core offering is its EdgeOps ML platform, which integrates DevOps, MLOps, and DataOps to manage AI/ML analytics across distributed environments.[1] Rather than relying on centralized cloud processing, Adapdix allows enterprises to harness data at the source—at edge devices and distributed infrastructure—enabling faster insights and more responsive autonomous systems.[1][2] This approach is particularly valuable for industrial firms and original equipment manufacturers (OEMs) seeking to implement AI capabilities into their products and operations at scale.[2]
# Origin Story
Adapdix was founded in 2015 during the early stages of edge computing adoption, when enterprises were beginning to recognize the limitations of cloud-only AI/ML architectures.[3] The company emerged from the Bay Area technology ecosystem, positioning itself at the intersection of three converging trends: the rise of IoT devices, the need for real-time decision-making in industrial settings, and the computational constraints of processing massive data volumes in centralized cloud environments.
A significant inflection point came in December 2020, when Adapdix completed an $8 million Series A funding round.[2] This was followed by backing from SoftBank's Opportunity Fund, validating the company's edge AI thesis and providing capital for platform development.[2] In February 2021, Adapdix acquired Edge Intelligence, a strategic move that merged data management capabilities with its existing AI/ML offering, strengthening its data-centric approach.[2]
# Core Differentiators
# Role in the Broader Tech Landscape
Adapdix operates at the center of a fundamental shift in how enterprises approach AI infrastructure. The edge computing market is being driven by several converging forces: the explosion of IoT devices generating data at distributed locations, the latency requirements of industrial automation and autonomous systems, and the cost inefficiencies of transmitting all data to centralized cloud environments.[1]
The company's timing is particularly relevant as digital transformation accelerates across manufacturing, logistics, and industrial sectors. Rather than competing in the crowded cloud AI space dominated by hyperscalers, Adapdix has carved a niche in edge optimization—a market segment expected to grow as enterprises seek to reduce cloud costs and improve real-time responsiveness.[1][2] By enabling companies to "harness data at the source," Adapdix is helping reshape how enterprises think about data architecture and AI deployment, moving from centralized to distributed intelligence models.[1]
# Quick Take & Future Outlook
Adapdix is well-positioned to benefit from the continued maturation of edge computing and the industrial AI revolution. As enterprises increasingly demand real-time, autonomous decision-making capabilities—whether in supply chain optimization, predictive maintenance, or connected vehicle systems—the company's edge-first platform becomes more strategically valuable.
The key question for Adapdix's trajectory is whether it can scale beyond its current Fortune 500 foothold into broader enterprise adoption while maintaining its technical differentiation against larger competitors entering the edge AI space. The company's acquisition strategy and SoftBank backing suggest ambitions to build a comprehensive edge AI ecosystem rather than remain a point solution provider. Success will likely depend on expanding its platform's versatility across industries and deepening integrations with the broader DevOps and data infrastructure ecosystem that enterprises rely on.
ADAPDIX has raised $10.0M in total across 2 funding rounds.
ADAPDIX's investors include BIP Capital, Nicholas Brathwaite, Celesta, Chris Kelly, Sasha Mirchandani, Andrew Byrnes, X2 Equity.