Growceanu is an angel investing platform. We provide end-to-end support for business angels to build a performant startup portfolio. Our processes are built to harness our investors' group expertise to truly help the entrepreneurs, lower the risk, and make higher returns possible. As an investor, you can apply at https://www.growceanu.com/for-investors As a founder, you can apply at https://www.growceanu.com/for-entrepreneurs

Growceanu Angel Investment

Kinled was founded in 1980 to retain the investment assets of the Aisher family.  In 1990, the investment focus shifted to a portfolio of early stage investments applying Kinled’s company development experience alongside the endurance and passion of family owned investments together with the strategic focus of venture capitalists.  Since then, Kinled has taken 8 companies to public markets and many more to successful trade sales at significant multiples.

Kinled Holding

Atlas Ventures is a venture capital fund that invests in early-stage technology companies

Atlas Ventures

Superseed Ventures is a venture capital fund and venture studio focused on investing, scaling and building trade tech startups. Superseed Ventures is the corporate venture arm of The Reece Group (ASX: REH).

Superseed Ventures

Forward Partners is a venture capital investment firm that seeks to invest in startups through venture funding, focusing on pre-seed and seed-stage investments in eCommerce, marketplace, Web3, and applied AI sectors.

Forward Partners

D2 is a venture fund for ambitious equity efficient start-ups

D2 Fund

OctaiPipe

OctaiPipe is a Federated Learning Operations (FL-Ops) architecture developed exclusively for Edge AIoT devices. It simplifies the entire lifecycle, from setup and training to smooth deployment, fine-tuning, and continuous learning. This allows data scientists to develop distributed IoT solutions that can learn and adapt at scale while lowering the cost of monitoring, maintenance, upgrades, and auditing.

Based in the UK, OctaiPipe (formerly known as T-DAB.AI) offers an end-to-end federated learning (FL) platform for building edge AI applications. The platform uses FL technology to train AI algorithms on-device at the edge without moving data to the cloud. The key features of the platform include network setup, training, deployment, AI model fine-tuning, and continuous learning. The platform is designed for building, deploying, and managing machine learning IoT solutions for critical infrastructure environments.

The toolkit to build machine learning applications at scale

Machine Learning Infrastructure

OctaiPipe raises £3.5 million to deliver secure Edge AI and lessen Cloud dependency for Critical Systems

Based in the UK, OctaiPipe (formerly known as T-DAB.AI) offers an end-to-end federated learning (FL) platform for building edge AI applications. The platform uses FL technology to train AI algorithms on-device at the edge without moving data to the cloud. The key features of the platform include network setup, training, deployment, AI model fine-tuning, and continuous learning. The platform is designed for building, deploying, and managing machine learning IoT solutions for critical infrastructure environments.Key customers and partnershipsAs of January 2024, the OctaiPipe platform was used by more than 20 customers and device OEMs.The company partnered with AIoT solution-ready package provider, EverFocus, to integrate its edge AI platform into their device portfolio. The combined offering will provide edge computing that has the necessary AI software to build, deploy, and manage ML models on edge devices for a wide range of use cases.Funding and financialsIn January 2024, Octaipipe raised GBP 3 million (~USD 3.8 million) in a <a href="https://sp-edge.com/updates/25844">pre-series A </a>round led by SuperSeed. The company had earmarked the funds to enhance OctaiPipe's proprietary Federated Learning technology and expand the accessibility of its platform to IoT-reliant critical industries. On the same date, it also received a GBP 500,000 (~USD 635,698) grant from Innovate UK.

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

OctaiPipe

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