Aindo, an Italian generative AI startup, has disclosed its successful €6 million Series A funding round, spearheaded by United Ventures.
In the latest funding round, Aindo, the generative AI startup based in Italy, welcomed participation from their existing investor, Vertis SGR, through the Vertis Venture 3 Technology Transfer initiative.
Aindo is on a mission to enhance the value of AI while respecting privacy. Their focus lies in pioneering the field of synthetic data and revolutionizing data mobility, which entails the secure sharing of information with a strong emphasis on safeguarding privacy.
This infusion of capital will empower Aindo to expand its workforce and advance AI solutions in critical sectors such as healthcare, finance, and public administration.
Daniele Panfilo, the co-founder and CEO of Aindo, expressed his enthusiasm about the funding, describing it as pivotal for the company’s growth during this crucial phase.
“Numerous organizations possess valuable structured information stored within their databases that remains untapped due to privacy constraints. The creation of synthetic data through generative AI offers a solution. The data regenerated by Aindo exhibits behavior akin to real data, all while preserving individuals’ privacy,” added Panfilo.
Giulia Giovannini, a partner at United Ventures, shared the excitement surrounding this investment, citing Aindo’s potential to address challenges posed by the AI revolution.
“The AI revolution continues to grapple with various obstacles, including data inaccessibility, protracted processing times, privacy concerns, and ethical dilemmas related to data collection. Synthetic data presents a solution to these issues,” Giovannini explained.
“We firmly believe that Daniele and his team possess the right ambition to expand the platform on an international scale within the realm of synthetic data. We are thrilled to contribute to this funding round, marking our inaugural investment from UV3, United Ventures’ recently launched fund,” Giovannini added.
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The Innovations Offered by Aindo
Aindo’s cutting-edge technology excels in producing synthetic data, an artificial data type capable of training machine learning models without the need for real-world data. Crafted through sophisticated algorithms, it can be customized to suit specific requirements, rendering it an invaluable asset for data scientists and machine learning (ML) developers.
Synthetic data opens doors for AI applications in crucial domains, including healthcare research and financial markets, with a potential for high-impact results.
Gartner, a renowned industry authority, identifies synthetic data as an emerging trend in Artificial Intelligence. Their projections suggest that by 2024, around 60% of data utilized in AI endeavors will be artificially generated, marking a significant leap from the meager 1% recorded in 2021.
In line with this growth, Grand View Research forecasts that the global synthetic data market is set to reach €1.79 billion by 2030.
Roberto Della Marina, Operating Partner of Vertis SGR and Managing Partner of Venture Factory, anticipates that in the coming years, the demand for synthetic data will become pervasive across sectors heavily reliant on data, particularly in healthcare, finance, and insurance.
“Aindo’s technology holds the potential to make a profound impact on the lives of millions,” Marina concludes.
Harnessing the Power of Synthetic Data
In the healthcare domain, synthetic data serves as a crucial resource for training AI models essential in the development of prognostic and predictive tools. These tools significantly bolster disease diagnostics and treatment strategies across a spectrum of medical conditions.
Within the financial sector, synthetic data assumes a pivotal role in shaping tailored solutions and services. Notably, developers harness synthetic data to construct precise risk prediction models that pinpoint patterns and behaviors associated with financial risk factors.
Finally, synthetic data emerges as an indispensable asset for facilitating smooth data exchange between public and private entities operating in the infrastructure and energy sector. Its applications extend to optimizing infrastructure and network management within smart cities and buildings, along with the monitoring of physical infrastructure. This resource also aids in assessing a range of risk scenarios, contributing to damage simulation and enabling optimal management evaluations.
