UFG Private Equity invests in fast-growing companies in Russia and CIS countries, focusing on Healthcare, BPO (Business Processes Outsourcing), Telecom & Technology, and Consumer Staples & Retail. The three UFG Private Equity funds have a total of $712 million of investments and commitments. UFG Private Equity also offers co-investment opportunities on larger deals.   UFG Private Equity was founded in 2005.

UFG Private Equity

Almaz Capital is an international venture capital firm investing in early stage, capital efficient technology companies. A unique bridge fund headquartered in Silicon Valley, Almaz Capital connects Eastern European/former Soviet engineering and entrepreneurial talent to the U.S. market and helps build global businesses. The firm invests in high-growth sectors such as artificial intelligence and blockchain applications, data engineering, cybersecurity, industrial IoT, networking architecture and infrastructure. Its investors include Cisco, the European Bank for Reconstruction and Development (EBRD), and International Finance Corporation (IFC), a member of the World Bank Group.  Founded in 2008, Almaz Capital has invested in over 30 portfolio companies and has had nine exits to date.

Almaz Capital

Decent Capital is a venture capital firm with a cross-border presence. It was founded in 2007 by Jason Zeng, co-founder of Tencent Holdings Limited.  Decent Capital has two teams, venture investment and asset management, with offices in Hong Kong, mainland China, Singapore and the United States. Decent’s investment team invests from Seed to Series A, primarily focusing on SaaS, Consumer Internet, and Frontier Tech.  Decent has invested in over 200 companies globally, spanning across the United States, China, Australia, Canada, United Kingdom, Hong Kong and Singapore. Decent upholds strong dedication to post-investment management, they continue to heavily support their portfolio companies’ business operations, supply chain and logistics, and future fundraising efforts. While Decent’s emphasis is on early-stage investments, they prioritize continuing to support portfolio companies throughout growth, for example, Decent invested in Lime from Seed to Series D.

Decent Capital

R136 Ventures is a multi-stage venture capital firm based in Woodside, California, primarily focused on enterprise SaaS, financial technology, data & analytics, cloud, artificial intelligence, machine learning, workflow automation and b2b marketplaces in the United States and Israel.

R136 Ventures

Western Digital (WD) develops, manufactures, and sells data storage solutions. Its solutions enable consumers, businesses, governments, and other organizations to create, manage, experience, and preserve digital content.

Western Digital

Comet Labs is an early-stage intelligent machines venture capital fund based in San Francisco. We help startups scale, guide innovation, and generate discussion in the fields of robotics and artificial intelligence. We're a venture fund and incubator founded in 2015. We invest in early stage artificial intelligence (AI) and deep technology companies transforming the world’s biggest industries. Our thesis is that AI and Robotics will revolutionize productivity in every vertical and unlock human potential. AI and machine learning technology have matured dramatically in just the past few years. The line of what is possible has been pushed back. However, it has become apparent that introducing AI to traditional industries requires a new and more collaborative approach. We started as a fund focused on investing in AI startups, but quickly understood that AI startups have little trouble raising money. Instead, they have a whole assortment of other common needs and challenges with building their businesses. That’s why we didn’t stop at the fund. We created the Labs programs, each providing an environment in which to build and grow companies. We partner with established industry brands to give startups access to vital resources and knowledge that otherwise would be out of reach. Together, we set up programs to help startups and partners discover new insights about how AI and robotics can transform industries like healthcare, agriculture, construction, transportation, and more. We’re always on the lookout for new thoughts and ideas about how AI will impact our society in the not-so-distant future. We’d love to hear yours.

Comet Labs

Established in 2017, the AI Alliance, LLC is a joint fund with Korean conglomerates SK Telecom, Hyundai Motor Company, Hanwha Asset Management and Element AI, world-renowned Canadian AI pioneer. The fund leverages each partner's unique value and targets the most promising sectors of commercial AI applications and infrastructure, specifically emerging AI-based technologies including autonomous vehicles, robots, manufacturing, drones, and AI hardware. The fund uses extensive domain knowledge and cutting edge research from Element AI to source, assess, and invest in startups focused on applying AI in advanced technologies. SK Telecom, Hyundai Motor Company and Hanwha's data and deep industry expertise combined with Element AI's artificial intelligence knowledge will both differentiate the fund in the market and add immense value to portfolio companies.

AI Alliance

Esperanto Technologies

Esperanto Technologies develops massively parallel, high-performance, energy-efficient computing solutions for Generative Artificial Intelligence / Machine Learning / HPC based on the open standard RISC-V instruction set architecture. Initially designed to meet the performance, power and total cost of ownership (TCO) requirements of large-scale datacenter customers, Esperanto’s inference chip is a general purpose, parallel processing solution that can accelerate many parallelizable workloads. It is designed to run any machine learning (ML) workload well, and to excel at Generative AI, one of the most important emerging AI workloads in datacenter and near edge applications.

Esperanto Technologies develops computing solutions for ML, AI, and deep learning (DL) applications based on the open standard RISC-V architecture. The company’s inference chip is designed to run any AI and non-AI workloads. The company converts trained models into an optimized package of code and data that runs on the architecture.

The toolkit to build machine learning applications at scale

Machine Learning Infrastructure

Esperanto Technologies and NEC Cooperate on Initiative to Advance Next Generation RISC-V Chips and Software Solutions for HPC

Esperanto Technologies develops computing solutions for ML, AI, and deep learning (DL) applications based on the open standard RISC-V architecture. The company’s inference chip is designed to run any AI and non-AI workloads. The company converts trained models into an optimized package of code and data that runs on the architecture.The company's ET-SoC-1 Chip has over 1,000 RISC-V cores on a single TSMC 7 nm chip to deliver tensor and vector acceleration optimized for ML/DL workloads. The company’s distributed memory architecture places on-chip memory where it’s required to improve processing utilization and remove memory bandwidth bottlenecks. The company also offers the ET-Minion core, which adds extensions optimized for ML, and ET-Maxion, which runs on the Linux operating system and implements advanced features, including prefetching algorithms. Esperanto’s network on chip (NoC) distributes operands and results between cores, caches, memory, and I/O.The processor also connects to an on-die hardware subsystem to prevent unauthorized or corrupted firmware updates while tracking user activity inside the chip. In addition, the company provides a GenAI appliance based on RISC-V that enables developers to create and deploy purpose-built vertical applications. The GenAI appliance is an integrated software/hardware solution that can be installed in private data centers or at the enterprise edge. It is preloaded and self-contained, offering high levels of data privacy and lower total cost of ownership while eliminating the need for developers to constantly download, port, and tune the latest LLMs and diffusion models to GPU-based hardware.As of January 2023, Esperanto operated in the US, Spain, and the EU. Key customers and partnershipsIts notable customers include Samsung, IFS, and Cadence.Funding and financialsIn November 2018, the company raised USD 58 million in Series B funding from strategic and venture capital investors. The funds were used to accelerate product development of its first generation 7nm solutions.

Datagen provides a self-service synthetic data platform that generates visual synthetic data of humans for computer vision applications. The platform allows users to write their own code to generate labeled synthetic data for faces, full body, and humans in motion in the form of images and videos to test and train AI models while retaining complete control over the data. The data covers a range of computer vision tasks including face landmark detection, face and hair segmentation, facial recognition and identification, full body pose estimation, full body segmentation, and behavior detection.Key customers and partnershipsThe company serves three of the top five global technology companies and Fortune 500 companies across the automotive, smart fitness, AR/VR/metaverse, smart office, and home security industries. Its notable customers include Google, Toyota, NVIDIA, IBM, and Intel.Funding and financialsIn March 2022, the company raised USD 50 million in a Series B funding round led by Scale Venture Partners. The funds were utilized to bolster its position in the market.Datagen reported to have recorded 8x growth in revenue in 2022.

Datagen

Weights &amp; Biases offers a platform that allows users to build models with experiment tracking, data set and model versioning, data visualization, hyperparameter optimization, and model management. The platform enables users to automatically retrain and re-evaluate models and centrally manage all model versions through every lifecycle stage—from development to staging to production. It also helps to visualize comparisons between model versions to evaluate performance and identify regressions before model deployment. It also offers toolkits such as <a href="https://sp-edge.com/updates/19172">W&amp;B Weave and W&amp;B Production Monitoring </a>to build and monitor data- and ML-powered applications, respectively, through interactive visualizations.The company also offers a <a href="https://sp-edge.com/updates/17880">suite of tools (W&amp;B Prompts) </a>to support large language model operations (LLMOps). W&amp;B Prompts includes tools and features for experiment tracking, debugging, and collaboration, as well as a one-click method to evaluate and score prompts through the OpenAI Eval’s framework.The company provides the platform through a free tier and under a subscription model for up to USD 50 per user per month.Key customers and partnershipsWeights &amp; Biases served more than 700,000 ML practitioners as of August 2023. Its notable include NVIDIA, Toyota, BMW Group, Samsung, Fujifilm, Truebill, and Clear Capital.The company has <a href="https://sp-edge.com/updates/18663">partnered with Lambda </a>, a GPU cloud computing provider, to offer its MLOps platform integrated with the infrastructure services of the latter, catering to enterprises dealing with AI and deep-learning workloads.Funding and financialsIn August 2023, the company raised <a href="https://sp-edge.com/updates/20874">USD 50 million in a strategic investment </a>round at a valuation of USD 1.25 billion, led by Daniel Gross and Nat Friedman. The round also saw participation from Sapphire Ventures, Coatue, Insight Partners, and other investors. The company aimed to utilize the fresh funds to further develop its ML tools, including the launch of W&amp;B Prompts.

Weights & Biases

BentoML offers an open-source platform called Bento Cloud, for the deployment and scaling of AI applications. It is a fully managed platform that handles the complexities of AI infrastructure, providing serverless autoscaling as required during the development and production stages. The platform helps to ship models to production with advanced deployment options including online and shadow A/B testing, while also providing visibility through model performance monitoring. The platform integrates with an organization’s existing data stack and enables users to continuously deploy and improve models in production. The company offers its platform through a combination of subscription and usage-based model.In addition, BentoML also provides the following open-source platforms: 1) Yatai, for model deployment on Kubernetes and 2) bentoctl, for model deployment on any cloud platform.Key customers and partnershipsAs of January 2024, BentoML reported that over 1,000 organizations, comprising more than 3,000 members, use its platform. Its notable customers include Koo, Open Layer, Line, and Naver.The company entered into a strategic partnership with TomTom, a developer of location technology, to help integrate with various models as well as in LLM deployments.Funding and financialsThe company raised <a href="https://sp-edge.com/updates/19684">USD 9 million in seed funding </a>in June 2023 led by DCM Ventures, with participation from Bow Capital. It aimed to channel the newly acquired funds to enhance its platform and offer developers a range of tools for building, deploying, and scaling AI applications efficiently.

BentoML

Iguazio provides a serverless platform based on its open-source Nuclio framework that helps users to develop, deploy, and manage real-time AI applications without requiring them to manually manage underlying infrastructures such as servers and storage. The platform creates real-time intelligence for applications located at the edge, public cloud, and on-premise data centers and reduces bandwidth constraints by consolidating data into a repository and making it available for applications as required. The platform can ingest and transform data from any source, which is then used to train, evaluate, deploy, and monitor models in production.The company leverages Big Data and ML software such as Apache Spark, TensorFlow, and Kubernetes to manage software containers that enable applications to run across different computing environments.Key customers and partnershipsIguazio serves customers in the financial services, telecommunications, smart mobility, manufacturing, retail, AdTech, gaming, healthcare, energy, and utilities sectors. Notable customers include Samsung, Microsoft, NVIDIA, AWS, Google, and Intel.Funding and financialsIn January 2020, the company raised USD 24 million in a funding round led by INCapital Ventures. The funds were utilized to accelerate its growth, expand its reach into new global markets, grow sales, and facilitate new sales channels that include companies such as Dell Technologies

Iguazio

Cogito Tech provides image and video annotation to generate computer vision training data sets for ML &amp; deep learning (DL). The video annotation solution leverages AI-powered tools to convert video into a sequence of images and generates training data sets for ML-based AI models, including self-driving cars and drones. The AI model understands and derives insights from visual data to detect, identify, classify, and track objects in an image or video. Cogito also provides virtual assistants and chatbots to help users in the process of training ML algorithms. Cogito Tech also provides high-quality, ethically sourced datasets that are compliant and reliable to train ML models.In addition, the company provides services to fine-tune LLMs and GenAI applications through reinforcement learning techniques as well as stress test models through red teaming.Key customers and partnershipsAs of December 2022, the company served more than 150 customers across 25 countries in the retail, medical, automotive, agriculture, robotics, smart cities, security, and manufacturing industries. Notable customers includes Verizon Media, The University of Sydney, National Geographic, Tumblr, and Vimeo.

Cogito Tech

Platforms and frameworks that help developers build and train ML models.

Platforms that deploy and host the trained and tested models in production and integrate them into applications through APIs.

Platforms that generate artificial data that mimics the characteristics of real data, which is then used to train ML models at scale.

Platforms that help monitor and manage ML models in training and deployment to track performance, conduct variance analysis, and optimize performance.

Makers of hardware such as CPUs, GPUs, and other application-specific hardware required to train and run ML workloads.

Integrated solutions that handle the end-to-end processes of developing, training, deploying, and monitoring ML models.

Data exchange providers and platforms that offer data labeling services via manual and/or software methods, which are used as input to train ML models.

Includes companies that provide infrastructure to build AI/ML applications

Provides tools to build, monitor, and deploy GenAI applications

Industry is focused on offering platforms for other businesses to build AI-/ML-powered apps

Esperanto Technologies

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