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LLM Adoption

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Streamlining the LLM product lifecycle

deepset Cloud allows your team to build complex LLM applications without friction and at a fraction of the time.

  • For Product Managers

    • Deliver value faster by adopting an iterative approach to building with LLMs

    • Facilitate collaboration and reduce misunderstandings between teams

    • Collect and analyze early end-user feedback for better prioritization

    • Optimize product development and increase ROI

  • For ML/NLP Professionals

    • Avoid the need to build models from scratch by leveraging pre-trained models and LLMs

    • Use pipeline templates for common use cases to optimize application architecture

    • Speed-up and simplify evaluation

    • Share experiments and prototypes with key stakeholders

  • For Software Engineers

    • Focus on business logic and features rather than the infrastructure

    • Quickly integrate LLMs into existing or new applications

    • Build for scalability and performance using production-ready backend architecture

    • MLOps for LLMs made easy — deploy fast while minimizing downtime

Discover deepset Cloud

From data to API-driven NLP backend services in no time.

Why our users love our product

1 / 1

We chose to add document similarity to our flagship product, because it's all about speed and efficiency — if lawyers need less time to research their cases, they have more time to acquire new clients. With deepset Cloud the advantage of using a pipeline with a fine-tuned language model was very clear to us.

Alexander Feldinger
Product Manager

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Frequently asked questions

  • Natural language processing (NLP) is a branch of AI that enables machines to process and interpret human language. In general, by implementing NLP, companies can leverage human language to interact with computers and data. Areas of NLP include semantic search, question answering (QA), conversational AI (chatbots), text summarization, document similarity, question generation, text generation, machine translation, text mining, speech recognition — to name a few use cases.

  • The ever-increasing volume of unstructured data in the enterprise, such as corporate documents, financial reports, research papers, and legal contracts, presents a challenging problem for enterprise product teams. Typically, this type of data is difficult for enterprise software to process. NLP allows developers to apply the latest research to industry-relevant, real-world use cases, such as semantic search and question answering. By streamlining approximately 80% of mundane processing tasks, NLP technology ensures better efficiency in data processing, data analysis, reporting, as well as improved customer experience, reduced operational costs, and increased customer satisfaction. Leveraging natural language processing, companies can create intelligent solutions for common business problems.

  • Modern enterprises have been rapidly integrating NLP into their internal products, processes and workflows. It is not only Amazon AWS or Microsoft who implemented NLP, but also enterprises such as Airbus, Infineon, Alcatel-Lucent, government agencies across the globe, as well as startup tech companies who are utilizing NLP to create amazing new products and services. NLP can be used in the financial industry, legal field, science, manufacturing, and many other verticals. Practical applications of NLP usually involve implementing semantic search and question answering to automate data analysis, decision-making process, fraud monitoring, claim management, regulatory actions, to reduce costs and improve customer satisfaction.

  • Natural language processing solutions (NLP) offer a variety of benefits of using artificial intelligence and machine learning tools to solve a range of common business and technology problems related to processing, sorting and making sense of data. We have helped the largest European companies and public sector organizations to instrument semantic search and question answering (QA) to automate data processing, legal analysis, regulatory compliance, and decision making. We've also built an enterprise SaaS product to complement our open source NLP framework (Haystack).

  • At deepset we believe in open NLP. This includes open language models, open source tools to build neural search and question answering, open communication and discussion, sharing experiences, as well as educating the developers and the users of NLP-enabled solutions. We believe that only through transparency and openness it is possible to apply natural language processing to various problems that enterprises and governments are facing.