[Funding alert] Amsterdam-based Orquesta Raises €800K in pre-seed Funding

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Funding round is used to develop the world's most innovative collaboration platform that empowers companies to seamlessly integrate and operate their products utilizing the capabilities of Large Language Models.

The rapid advancement in generative artificial intelligence and the emergence of Large Language Models (LLMs) have led to explosive growth in the number of companies adopting this relatively new form of AI.

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The World Economic Forum even predicts that these tools, which utilize both external and internal datasets to generate new content such as texts, summaries, translations, and inputs for chatbots, will replace 44% of today’s work activities by 2027.

co-founder of Orquesta Sohrab Hosseini said, “The accelerated development of new AI functionalities, leads to increased fragmentation, complexity and a need for speed within the technology landscape for our clients. This prevents the already scarce talent of our clients from focusing on strategic work and growth. With one LLM gateway, including all the necessary tooling, Orquesta is their partner to remain competitive.” We are very excited about the focus and multidisciplinary experience of the various VCs and Angels who match and support our level of ambition.

Herman Kienhuis, managing partner of Curiosity VC: "The availability of Large Language Models (LLMs) offers tremendous opportunities. With Orquesta, Sohrab and Anthony have developed a no-code platform that allows companies to easily configure, test, and manage complex prompts and various LLMs, leading the way in the market to assist companies in the application of these new Generative AI technologies."

About Orquesta

Orquesta was founded in 2022 by Sohrab Hosseini and Anthony Diaz. It empowers companies to seamlessly integrate and operate their products using the capabilities of Large Language Models through a unified collaboration platform.

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The platform centralizes prompt management, streamlines experimentation, collects feedback, and provides real-time insights into performance and costs. It's compatible with all major Large Language Model providers, ensuring transparency and scalability in Large Language Model Operations (LLM Ops), ultimately leading to shorter customer release cycles and reduced costs for both experiments and production environments.

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