AI
Search
AI search makes it possible to find precise answers and relevant content across systems – quickly and contextually. Instead of classic keyword-based search, the solution uses language understanding and semantics, allowing users to ask questions in natural language and receive meaningful results.
- Examples of solutions
From question to action – automatically and intelligently
Semantic search and internal knowledge access
This type of AI search understands the meaning behind the user’s question – even if the words don’t match exactly. Users can ask questions in natural language and receive relevant answers based on the content’s meaning, not just its wording. The search can run across documents, intranets, and databases, making it ideal for knowledge-heavy organizations where quick access to accurate information is critical.
AI recommendations and estimation
This solution uses AI to suggest next steps based on the user’s input, behavior, or profile – such as products, articles, or services. It can also function as a price estimator, where the user enters their needs and the system returns a qualified estimate. The recommendations are data-driven and continuously updated, ensuring they remain relevant and personalized – increasing both conversion and engagement.
Retrieval-Augmented Generation
RAG combines search and generative AI, allowing the chatbot or assistant to provide answers that are both accurate and verifiable. The model retrieves knowledge from your own documents and data, generating responses with references to the source. This makes the solution ideal for use cases such as support, case handling, or internal knowledge sharing – where accuracy and traceability are essential.
Knowledge-based assistants
This type of AI assistant helps users find answers, navigate complex materials, or gain an overview of rules, processes, and documents. It can be used on intranets, in employee portals, or as a support tool for helpdesk teams. The assistant connects to your internal knowledge and communicates in natural language – making it an effective tool for onboarding, self-service, and faster internal clarification.
Product and content personalization
This solution customizes the display of products, articles, or features based on the user’s behavior, preferences, or past interactions. The AI can highlight the most relevant content in real time – for example, on your webshop, knowledge base, or customer portal. It increases relevance for the user and enhances both the user experience and conversion rate – without the need for manual audience setup.
Content sorting and tagging
AI can automatically analyze and categorize large volumes of content – such as articles, documents, products, or media files. It assigns relevant tags, topics, and metadata, making the content easier to find, reuse, and present in the right context. This minimizes the need for manual maintenance and ensures that your knowledge and materials are better utilized – both internally and externally.
- Benefits of AI
Benefits of AI search and recommendations
AI search and recommendations aren’t just about finding information – they’re about doing it faster, smarter, and in a way that’s more relevant to the user. When you apply artificial intelligence to your search functions and recommendation engines, your knowledge and products become significantly more accessible and useful.
At AIgentur, we help organizations build intelligent search solutions that enhance clarity and strengthen both the customer experience and internal efficiency. Here are five key benefits our clients typically experience:
Faster access to knowledge
The user gets precise and relevant answers – without needing to know the right keywords or menu items.
Personalized experience
Recommendations and search results are tailored to fit all of the user's needs, behavior, and history.
Better use of data
Documents, guides, and products become easier to locate, access, and utilize, whether by internal teams looking for information or external stakeholders needing interaction with your resources.
Less support and friction
When users are able to find answers on their own, it not only empowers them but also significantly reduces manual inquiries, helping to decrease wasted time and improve efficiency.
Scalable delivery
Search and recommendation systems operate continuously, seamlessly integrating across different platforms and systems, all while eliminating the need for extra resources or manual intervention.
- Our process
How we create AI solutions that work in practice
At AIgentur, we follow a structured and transparent process to ensure that the solution fits your needs – and delivers value from day one. We believe in close dialogue, clear agreements, and ongoing quality assurance.
The process is carried out in six phases and can be adapted to both small and large projects.
1. Scoping
2. Approval
3. Development
4. Test
5. Feedback
6. Implementation and onboarding
- Contact us
Ready to get started?
Let’s take the first step together
At AIgentur, we meet you where you are and help turn your goals and challenges into concrete solutions.
You might already have an idea or a specific need. Or you might just be curious about how AI can be used in your business.
Either way, we’re happy to have a no-obligation conversation – and show you how a structured and flexible approach can deliver real results.
Book a meeting
- Frequently asked questions
From curiosity to clarity in seconds.
What is AI search?
AI search uses artificial intelligence to understand the user’s intent and context – rather than just matching keywords. This means users can phrase questions in natural language and receive more relevant, precise, and useful answers, even to complex queries.
What is semantic search?
AI search presents existing results from your content, while RAG (Retrieval-Augmented Generation) combines search with generative AI to provide the user with a formulated answer that includes source references. RAG is ideal when direct answers are needed – not just a list of search results.
What is the difference between AI search and RAG?
AI chatbots work by combining language models with data and business logic. The user types a question in natural language, and the chatbot analyzes it, finds relevant information, and formulates a response. Modern solutions use generative AI and can be connected to your own documents and systems.
How do AI recommendations work?
AI recommendations use data about the user’s behavior, preferences, and input to suggest relevant content, products, or actions. This can include “Others also bought…”, personalized article suggestions, or tailored campaigns – all updated in real time.
What does it take to get started with AI search?
We start by analyzing your existing content, data structure, and needs. Then we help you choose the right model and set up the search solution to match both your users and systems. You don’t need to have everything ready – we’ll guide you through the process.
Can we control what the AI is allowed to show or suggest?
Yes. We establish clear boundaries for which data sources and content types are used, and what the AI is allowed to respond to. You can exclude specific documents, adjust the tone, and ensure that responses align with your brand, policies, and compliance requirements.
