[go: nahoru, domu]

Build, Test, and Deploy Generative AI in Your Mission-Critical Business Applications

Trusted by

  • SOC 2 Compliant
  • GDPR Compliant
  • CSA STAR Level 1 Certified
Upcoming Webinar: Accelerating AI in Publishing [Tuesday, 17th July]
Real-world case study: How YPulse launched a new AI-powered offering to empower their subscribers in under 120 days

Meet deepset Cloud, built with Haystack

deepset Cloud accelerates the development and deployment of custom business applications with large language models (LLMs). Built using Haystack, deepset's leading AI framework, deepset Cloud enables fast and accurate prototyping, building, and launching of advanced AI applications.

Streamline the AI Application Development Lifecycle for Rapid, Reliable Adoption

  • Build fast.

    Get the processes, tools, and expertise you need to move quickly and confidently from prototype to production – all in one place.

  • Launch with confidence.

    Optimize your solution’s accuracy, performance, and cost with industry leading testing and observability. Then, go live at any scale in one click.

  • Be in control.

    Make AI innovation your advantage. Escape vendor lock-in and explore new models and configurations with your data, without limits.

  • Grow your expertise.

    Extend your team with access to our world-class AI engineers. Get guidance and support every step of the way, from ideation to implementation and beyond.

Generative AI Best Practices

Gartner®: How to Pilot Generative AI

Are you looking to implement generative AI but struggling to get started? Hear from experts on how to achieve faster development cycles and prioritize use cases for maximum business impact.

Research

Gartner

Download the Report

O'Reilly: LLM Adoption in the Enterprise

Explore the process of building with LLMs with a product mindset. Discover the underlying technology, the necessary cross-functional resources, and the practical application of various use cases.

Guide

O'Reilly

Download the Guide

Speed up Your AI Development Cycle

Deliver AI innovation with one collaborative platform for your product, AI, and business teams.

Connect Your Data

  • Enhance LLMs with business and customer context.
  • Ingest all unstructured and structured data.
  • Process, vectorize, and index data at 10x speed.
  • Integrate with any of your existing data sources.

Optimize Your Models

  • Pick from leading AI models like GPT-4, Llama-v2, Falcon, Claude or bring your own LLM.
  • Draw from a library of application templates and building blocks, from data pre-processing to hybrid retrieval.
  • Optimize prompts in a dedicated prompt engineering playground.
  • Fine-tune models in GPU Notebooks.

Prototype and Test

  • Run structured experiments with simple configuration and comparison tools.
  • Assess performance using both traditional and model-based metrics.
  • Share prototypes to collect user feedback.
  • Iterate and evaluate quickly to create production-ready solutions in record time.

Deploy and Monitor

  • Deploy to production with one click.
  • Query your pipeline via REST API.
  • Leverage auto-scaling infrastructure to adjust to your traffic.
  • Monitor performance metrics including groundedness, requests, latency, and usage.

Transformative AI Applications for Enterprises

Examples of our customers launching category-defining AI solutions.

See How deepset Cloud Can Help You

Learn how you can build enterprise-grade production applications with full speed and trust.

Get In-Depth Learning & News in Your Inbox

Subscribe to our newsletter and receive valuable articles on LLMs, AI applications and industry updates from our team.

Modern AI Architectures at your Fingertips

Go custom or choose from templated LLM-solutions to solve any use case – from simple to complex.

Retrieval Augmented Generation (RAG)

Retrieve relevant documents, use them to inform responses, and generate accurate and contextually rich answers to complex queries.

Agents

Automate and streamline tasks, workflows, and insight generation with an compound AI system capable of complex reasoning and decision making.

Conversational BI (Text to SQL)

Transform natural language queries into SQL commands, ask questions of complex datasets, and make data analysis more accessible and intuitive.

Question Answering

Pinpoint the exact location of an answer, efficiently extract and cite information, and generate human-like responses to questions.

Vector-Based Search

Understand and retrieve information based on semantic similarity, deliver highly accurate and relevant search results, and provide an advanced recommendation service.

Multimodal

Integrate and process multiple forms of data including text, PDFs, images, audio, and video for an enriched user experience.

Learn with Our Team of Experts

Go from basic to advanced topics to become a better AI builder.

Next-Gen Finance: Top LLM Use Cases to Work Smarter

Discover how you can use LLMs to gain insights, accelerate due diligence, automate manual tasks, and deliver differentiated value clients in the financial services industry.

Webinar

On Demand

Watch Now

How to Detect Hallucinations in LLM-based Apps

Hallucinations are one of the biggest challenges in running LLM-based applications such as retrieval-augmented generative QA. Hear current approaches on how to systematically detect and avoid hallucinations.

Webinar

On Demand

Watch Now

5 Steps to a Successful Gen AI Pilot

Go from prototype to production with your AI initiatives in 5 clear steps. Gain practical advice on selecting use cases, structuring your team, and conducting live user tests.

Webinar

On Demand

Watch Now

News, Trends, and Resources

Get the latest news, strategies, and guides to help you achieve your AI goals.

Build Powerful AI Applications with Haystack by deepset & NVIDIA NIM

deepset and NVIDIA’s new partnership empowers enterprises to launch and scale powerful AI applications.

News

Ecosystem

Read the News
A flowchart depicting the process of information retrieval and answer generation in a RAG pipeline. It starts with "Query" in an oval, leading to a rounded rectangle labeled "Retrieval", indicating the process of sourcing information from a database. An arrow then points to another oval labeled "Prompt", which leads to a box with rounded corners labeled "LLM", symbolizing the language model. From the LLM box, an arrow points downwards to an oval labeled "Answer", representing the final output. IA flowchart depicting the process of information retrieval and answer generation in a RAG pipeline. It starts with "Query" in an oval, leading to a rounded rectangle labeled "Retrieval", indicating the process of sourcing information from a database. An arrow then points to another oval labeled "Prompt", which leads to a box with rounded corners labeled "LLM", symbolizing the language model. From the LLM box, an arrow points downwards to an oval labeled "Answer", representing the final output. I

How to Evaluate Large Language Models

Read about the unique challenge of evaluating the output of generative AI models in RAG pipelines – and the most promising solutions to solve these challenges

Blog

Guide

Read Now

Fine-tuning Large Language Models

All about fine-tuning. Understand when is it useful, and when should you use other techniques like RAG to improve your LLM output.

Blog

Guide

Read Now

Real World Industry AI Use Cases

LLMs for Media & Publishing

How media teams are using LLMs to generate content audiences want, in a way they can easily access.

Industry Brief

Media & Publishing

Read Now

LLMs for Finance

How LLMs can process massive amounts of information and deliver competitive advantages in the finance sector.

Industry Brief

Finance

Read Now

LLMs for Legal

How LLMs are powering innovative IT products to expedite the work of legal professionals.

Industry Brief

Legal

Read Now

See How deepset Cloud Can Help You

Learn how you can build enterprise-grade production applications with full speed and trust.

Frequently asked questions

  • AI helps businesses enhance customer experiences, automate repetitive tasks, improve decision-making processes, and increase operational efficiency. With Generative AI, companies can process large volumes of data quickly, create conversational AI experiences for customers, gain insights from data analytics, and streamline various business operations.

  • AI can enhance customer service through chatbots, virtual assistants, and automated support systems. These tools provide instant responses, handle common queries, and share tailored recommendations or suggested workflows. All of this is achievable with improved response times, leading to higher customer satisfaction and reduced workload for human agents.

  • AI benefits various industries such as finance, insurance, legal, media, publishing, healthcare, retail, manufacturing, consumer goods, technology, and logistics. In finance, AI automates manual processes with intelligent document processing. In insurance, AI can expedite claim management. In healthcare, it aids in diagnostics, patient care, and clinical trial analysis. Retail benefits from personalized marketing and support. Technology and manufacturing see improvements in sales, user interfaces, and self-service support. Media and publishing gain in subscriber conversion and retention through personalized content discovery.

  • Natural language processing (NLP) is a branch of AI, that includes the use of AI LLM technologies, 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.

  • Businesses can start by identifying key areas where AI can add value, such as conversational interfaces on their website and/or products, customer service, data analytics, or business process automation. Partnering with AI solution providers like deepset can help businesses understand, test, and integrate AI technologies effectively and efficiently into customer experiences and business workflows at a fraction of time and cost of building out an AI development infrastructure.

  • deepset offers two AI development and deployment tools: Haystack and deepset Cloud. Haystack, an open-source AI orchestration framework, enables top developers to innovate with AI. deepset Cloud, a comprehensive commercial SaaS platform, allows businesses to build, test, and deploy generative AI applications to enhance customer-facing products and productivity workflows. These tools support advanced AI solutions such as retrieval-augmented generation (RAG), AI agents, semantic search, question answering, text-to-SQL, and document processing.

We use cookies

for analytics and to improve website experience.