The starting point
An interlocutor to give shape to the problem
A project does not always start from a request that is already defined. Sometimes all you know is that a process slows the team down, that information is scattered or that a good idea has not yet found a concrete form. AntasAI presents itself as an Idea Engineer: it guides the exploration of the problem through a conversation. Its interest is not only the first question, but the context that makes it important. To begin you can describe what happens today, who runs into the difficulty and what change you would like to see. It is a way to make assumptions more explicit before turning them into a development request.
Diagnosis and investigation
First understand, then propose
AntasAI's public path separates an initial diagnosis from an investigation phase. The conversation starts from the operational challenge and continues with follow-up questions. An example starting point could be: “Our team spends too much time looking for the information needed to answer customers”. From here, the useful aspects to clarify are the available sources, the people involved and the exceptions that make the work difficult. This example illustrates how to frame the discussion; it is not a real conversation or a client result. The more concretely the problem is described, the easier it becomes to assess whether a proposal really answers the initial need.
Three perspectives
Quick Win, Scale and Disruptive
AntasAI organizes proposals into three directions: Quick Win, Scale and Disruptive. The first looks at an immediate intervention, the second at growth and the third at a more radical change. Consider them perspectives to compare, not three promises of results. A small solution may be the right place to start; a more ambitious one may require data, time and responsibilities that are not yet available today. To choose, ask yourself which hypothesis you want to verify first, how you will recognize an improvement and how much work is needed to try it. The value of the comparison lies in making alternatives and trade-offs visible, leaving the team to decide what to explore further.
Context and knowledge
The role of the RAG knowledge base
AntasAI's presentation includes a knowledge base fed by PDFs and URLs. The declared mechanism is RAG, retrieval-augmented generation: before formulating an answer, the system can retrieve relevant information from a collection of content and bring it into the model's context. The principle is different from training a new model from scratch on every document. For someone using an assistant, it means being able to discuss a problem with references closer to the field of work. Quality remains tied to the available sources: incomplete, outdated or ambiguous documents can limit the usefulness of the answer. This is why references should be read and verified when they become the basis for a decision.
From discussion to work
Collecting proposals and defining the next step
The path described on the AntasAI site ends with an email report that gathers solutions, sources and next steps. The report can become a starting point for an internal discussion: which information is missing, which proposal deserves a trial and who can evaluate it? It does not replace a technical analysis of the existing software or an agreed estimate of time and costs. Before turning an idea into a project, it is useful to define a limited case, the people who will try it and the result to observe. It is the move from generating possibilities to verifying them: a proposal gains value when it is compared with data, constraints and real work.
An example of a vertical chatbot
From a generic answer to a conversation path
AntasAI shows a specific application of business chatbots: guiding the user in exploring a problem. A vertical chatbot has a recognizable task and path, instead of presenting itself as a universal answer to every question. In this case the common thread is the progression from diagnosis to alternatives. To design a similar experience in a company, you need to clarify who it is for, what information it needs and when to involve a person. Language matters too: understandable questions and well-calibrated requests for context help the user take part. If you want to evaluate an assistant for your process, we can start from these elements and discuss a concrete scope.
Outcomes
What you can explore with AntasAI
- Put an operational challenge and the desired result into words.
- Compare three perspectives before choosing what to explore further.
- Bring proposals and references into a discussion with your own team.
Talk about your project
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