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iFinder & generative AI for success: Smart search, precise answers
Are you considering the use of generative AI, or currently working on projects involving large language models like GPT and would like to learn how these can be integrated with iFinder? We’ll be happy to discuss tailored AI application opportunities for your specific use case.
Save time and resources with AI Language Models
Large Language Models make it easier to deal with large volumes of documents, improve search, summarize relevant text and provide valid answers to your questions
- Do you work with large volumes of data and documents?
- Do documents need to be processed or evaluated quickly and effectively?
- Large amounts of text and document content need to be summarized (e.g. as a support for the creation of notes/memos)?
- It is important to you that
- the summaries contain accurate information from the organization's own sources,
- the user's access authorizations are taken into account and
- the sources are transparently traceable? - You want to retrieve information from large amounts of data, e.g. via dialog with a chatbot (question answering system)?
Good to know: Large Language Models I LLMs I AI Language Models I Generative AI I Retrieval Augmented Generation (RAG)
Large Language Models (LLMs) are based on artificial intelligence and machine learning. They understand longer texts and are able to create a concise and summarized version with the key messages of the original text. Based on relevant search results, LLMs generate natural language answers.
Generative AI (GenAI) refers to AI-based systems that use machine learning and large amounts of training data to generate new content, such as text, images, videos, or code. In the context of search, generative AI is primarily used to generate answers based on search results as well as for their enrichment.
The term Retrieval Augmented Generation (RAG) is important for Natural Language Processing. RAG combines the strengths of search and LLMs. Using existing information, the model is able to better understand the context of user queries and generate more accurate and relevant answers.
Generative AI (GenAI) refers to AI-based systems that use machine learning and large amounts of training data to generate new content, such as text, images, videos, or code. In the context of search, generative AI is primarily used to generate answers based on search results as well as for their enrichment.
The term Retrieval Augmented Generation (RAG) is important for Natural Language Processing. RAG combines the strengths of search and LLMs. Using existing information, the model is able to better understand the context of user queries and generate more accurate and relevant answers.
Choosing the right LLM – Let us guide you
GPT, Gemini, Claude – or perhaps one of the smaller models? Don’t worry, we’ll keep track of it all for you.
As a provider of Enterprise Search software with an integrated AI assistant, we continuously evaluate emerging technologies on the market. We’ll help you select the model that best fits your use case and IT infrastructure – whether on-premises or SaaS.
Enterprise Search & generative AI: The perfect synergy
By combining search and language models, organizations can break down data silos, unlock the full potential of their data, simplify effective knowledge discovery, and boost productivity. Search ensures optimal preprocessing of data and provides generative AI with the relevant information needed to answer user questions securely and in compliance with data protection regulations.
Real-World Customer Example: Successful Use of AI in a German Federal Authority – From AI-Powered Search to an AI-Based Intranet Chatbot
Our customer has been using iFinder for many years as an internal search solution to efficiently provide information.
In a new project, generative AI is now being introduced in the form of an intranet chatbot to assist employees with answering organizational questions—quickly, accurately, and around the clock.
In a new project, generative AI is now being introduced in the form of an intranet chatbot to assist employees with answering organizational questions—quickly, accurately, and around the clock.
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The Challenge:
The goal is to enable employees to ask common work-related questions in natural, and sometimes informal, language—and receive answers in the same natural tone.
All chatbot responses are strictly based on intranet data. If a question cannot be answered using the available internal content, the chatbot clearly communicates this and does not generate or fabricate a response ("no hallucination").
All chatbot responses are strictly based on intranet data. If a question cannot be answered using the available internal content, the chatbot clearly communicates this and does not generate or fabricate a response ("no hallucination").
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The Solution:
The entire system is designed to handle several thousand queries per day. The combination of iFinder and iAssistant ensures that all responses are strictly based on data for which the requesting user has the appropriate read and access permissions.
The technological expertise of IntraFind, the subject-matter knowledge of the federal authority, and a strong, collaborative partnership were all essential to the success of the project.
The technological expertise of IntraFind, the subject-matter knowledge of the federal authority, and a strong, collaborative partnership were all essential to the success of the project.
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Of course, the use case presented can be applied to any industry—whether manufacturing, the financial sector, or public administration.
Do you have your own special use case?
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You want to use AI tools like LLMs for language processing?
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Contact us via the form providing the rough key data of your use case
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We analyze your use case and offer you tailored, non-binding advice.
Drive AI innovations with us - and stay in control of your data
What we offer
- A non-binding, concise assessment of which solution is right for your case and your organization
- Explanations of benefits and points to consider (e.g. data security and privacy)
- You do not enter into any obligations, we analyze your use case and advise you
- Get in contact with:
Franz Kögl, CEO Breno Faria, Product Lead AI
