AI Examples from the World's Biggest Health Care Companies

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At Manceps, we’re on a mission to help companies build and deploy AI solutions at scale. One thing our clients ask us all the time is—

WHAT CAN YOU ACTUALLY DO WITH AI?

Going company by company, we were surprised and delighted by all the ways that Artificial Intelligence and Machine Learning are helping manufacturers make BIG CHANGES.

We’re talking skyrocketing throughput, optimizing the supply chain, and accelerating research and development.

This deeply-sourced ebook covers more than a dozen companies and use cases.

We hope you enjoy taking a peek into the future!

Featured Companies

and many others!

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In an era dominated by cloud computing, there are still compelling reasons to host AI models on-premises. While cloud-based solutions offer scalability and convenience, certain environments demand more control, reliability, and privacy. Hosting models locally ensures greater data governance, allows compliance with industry or regulatory standards, and enhances security by keeping sensitive information within a closed network. It also becomes essential in situations where internet connectivity is unreliable or unavailable, such as in remote facilities, secure government operations, or offline field deployments. Additionally, on-prem hosting can offer reduced latency, cost predictability, and full control over model execution and updates—making it a critical choice for organizations with strict operational or compliance requirements. This will show you how to run a basic document Q&A offline using: Ollama + local LLM (Gemma3, Mistral, Llama3.3, etc.) LangChain FAISS (vector DB) SentenceTransformers (embeddings) PyPDF (PDF loading)

How to extract knowledge from documents with Google PaLM 2 LLM

PaLM 2 is Google's next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than previous state-of-the-art LLMs. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements. This article offers a quick and straightforward method for leveraging the PaLM 2 API to extract knowledge and ask questions from text.

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