
On-site search that sells: from query to conversation
Searching products and knowledge is only the beginning. See how to connect it with an assistant, recommendations and actions in the frontend.
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Searching products and knowledge is only the beginning. See how to connect it with an assistant, recommendations and actions in the frontend.
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Transcribing meetings and sales calls can bring order to your work, but it requires consent, retention rules and access control.
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RAG adds knowledge, fine-tuning changes behaviour. Confusing the two leads to expensive deployments that don't solve the problem.
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Chunking, embeddings, hybrid search and reranking. A practical guide to RAG that answers from sources.
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Open source gives you control; closed models give you convenience and quality. The decision depends on your data, budget and who will maintain the infrastructure.
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Not every task needs the largest model. How to split tasks, calculate costs and protect data when choosing between local and frontier models.
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An LLM understands language, but it should not decide on an action by itself. Here is where text generation ends and the decision layer begins.
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Not every problem needs a model. Sometimes a form, a rule, or a tidier process is enough. Here are six signals that AI is the wrong first step.
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Don't measure the number of messages. Measure whether customers find answers faster, whether the issue gets resolved, and how much support costs.
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Human in the loop is not a checklist. It is a decision design: what AI prepares, what a person approves, and when the process must stop.
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Prompt injection, data leaks, and excessive agent autonomy. How to deploy AI in a company without giving up control.
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Automation does not have to mean a chatbot that handles everything. We show which cases can be handed to AI and which are better passed to a human.
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An agent with access to everything is fast only until the first incident. How to design autonomy you can actually control.
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