Search was designed for a world where you knew how to phrase the right keyword. It works well if you already know roughly what you are looking for and how it is likely to be described. It works less well for the kind of questions that actually arise in the middle of complex work — questions that are half-formed, context-dependent, or genuinely open-ended. askFinz Search is designed for those questions.
What is different about an AI search engine
Traditional search returns links. You do the work of reading them, triangulating across sources, and assembling an answer. That was a reasonable bargain when the alternative was a card catalogue. It is a less reasonable bargain when you have ten such questions in an afternoon.
An AI search engine starts one step further along: it reads the sources and returns an answer. The sources are still there — you can still read them — but you are not required to do that work for every question. When you do want to go deeper, the trail is already laid.
Built for people and for the agents working alongside them
Search in a modern working environment is not only done by people. AI agents researching on your behalf, LLMs retrieving current information to ground their answers, automated workflows that need reliable factual input — all of these are now part of how knowledge work gets done.
askFinz Search is built to serve all three:
- People doing real work. Ask a complex, contextual question and get a synthesised answer rather than a set of links to read.
- AI agents working on tasks. Agents in your askFinz workspace can call on search as a capability — grounding their work in current, sourced information rather than working from training data alone.
- LLMs that need current context. For tasks where what happened this week matters, search provides the grounding that a model's training data cannot.
What this means for your working day
- Ask questions, not keyword strings. "What are the current regulatory requirements for X in Y jurisdiction?" is a valid search. You do not have to reduce it to three words and hope.
- Get a direct answer, not a reading list. For the majority of questions, you want the answer, not twelve articles that might contain it.
- See the sources. For anything important, the sources are attached. You can verify, dig deeper, or cite them in your own work.
- Let agents search for you. When AI in your workspace needs current information, it draws on the same search capability — so you are not manually researching inputs to tasks you have already delegated.
Part of a connected workspace
Search is most useful when it connects to the rest of what you are doing. A search result that informs a research brief, feeds into a document, or becomes an input to a model conversation is more valuable than one that ends in a tab you close. Because Search is part of the same workspace as everything else, that connection exists by default.
For what that workspace looks like in practice, see AI research that cites its sources and One workspace instead of app-switching.
Further reading
- Search — the AI search engine in askFinz.
- AI research that cites its sources — when a search question becomes something worth investigating in depth.
- One workspace instead of app-switching — why search should be part of your workspace, not separate from it.
- The Stanford Internet Observatory publishes clear-eyed analysis of how search and AI-generated content interact in practice.