RESOURCES
Read about some of the client engagements we've performed to get a better sense of the kinds of problems we solve and what it's like to work with Manifold.
Our client is an oil and gas optimization company that designs, manufactures, and leases wellhead gas compressors and remotely monitors equipment operations. To enhance their software platform, Manifold built an analytics engine that uses operational data to predict and prevent machine down-time.
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NovoLux had developed unique core technology for an intelligent natural lighting product. They approached Manifold to accelerate product development. We worked with them to take a prototype implementation and transform it into a shippable, scalable, upgradable product.
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Better Therapeutics treats patients who suffer from chronic diseases. A top priority in their strategic plan was improving care through more personalized therapy. Manifold built a HIPAA-compliant, AI-powered product to deliver customized treatment plans for their patients.
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Babylist is a leading US baby registry that wanted to gain insights into their best customers and develop strategies to maximize desired shopper behaviors. Manifold built a predictive machine learning model that runs analyses on-demand in order to optimize day-to-day strategic decisions.
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Highfive is a business video conferencing platform. They wanted to tap into insights about their customer’s needs and behavior buried in product usage logs. Together, Highfive and Manifold performed exploratory analysis and feature engineering using Google Cloud's BigQuery.
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Our client, a Fortune 100 technology company, wanted to simulate wireless performance and compare two different modes of operation for the purposes of research and development. Manifold built a multi-user simulation of the 802.11AF MAC (medium access control) layer to answer their question.
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Our client—a wireless network operator—wanted to add additional capabilities to their business building wireless systems for IoT connectivity. Manifold guided the complex, year-long project to build a software defined radio (SDR) that could transmit over the air (OTA) in real-time from aspiration to reality.
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We developed some internal tools for easily spinning up Docker-based development environments for machine learning projects. In order to help other data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced our evolving toolkit, Orbyter.
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