Swiss Alps, Overlooking 14K-Foot Peaks
The general concept of ai-one's CORA
The CORA ai-workflow platform is easy and intuitive to use. The menus are dynamic and can be operated on computers as well as on tablets and smartphones
CORA = Communicate ● Orchestrate ● Reasoning ● Act
The full-stack intelligence process
describes a method where data from various sources (e.g., databases, APIs, social media) is integrated into a single platform to gain a comprehensive view of a topic or situation. This process typically includes steps such as data collection, processing, analysis, and presentation. The goal is to gain a comprehensive insight into the topic and make decisions based on comprehensive data.
Your AI must integrate into your company and act like a group of collaborating expert individuals! Which constantly improves and adapts to the changes in the company and the environment.
Harmonization between leading and competing systems!
The big users of data collections are already known. However, many data collections are based on old technology and were created without modern concepts. A transition to new systems is time-consuming and expensive. Often, there is a lack of know-how about the old data collections. When migrating to modern systems, duplicates and unvalidated data threaten to impair the quality. Nevertheless, important information is hidden in old repositories.
Knowledge Horizon – Why It Matters
The knowledge horizon of an AI system defines which questions it can answer—and where its limits lie. Only if a question falls within this horizon can the AI provide a reliable and meaningful response. Without the necessary knowledge base, answers become vague or incorrect.
Equally important is the overlap between the human and AI knowledge horizons. Effective communication is only possible when both share at least some common context. Without this overlap, misunderstandings occur—or no meaningful interaction can take place at all.
To use AI effectively, you must understand what it knows—and what it doesn’t.
Plausibilization and validation
Sources: It must always be possible to trace which sources the answers are based on. Both for knowledge queries and for matcher queries, the system must be able to point to the sources.
Semantic History: All answers generated by an AI system should be marked with their origin and how they were created (Human, Chat-Bot, internal database, external database, etc.)
Plausibility: refers to how believable, logical, and coherent a text appears at first glance. AI-generated texts often seem very plausible - they are linguistically convincing and well-structured. But that doesn't mean they are factually correct.
Truth or fact: is what can be objectively verified - through sources, data, or expertise.
Single point of access
On AI access point to provide subsequent applications and systems with the optimal information for the next process steps.
CORA-Workflow generates content for the next process steps through its central AI functions.
Digital Intelligence combined, as coach and prompter for safe and efficient use of AI.
Natural dialogues for intuitive workflows with a central control.
CORA-Workflow analyzes and provides context-rich results for targeted actions, automatically, traceable and effective.
The market is currently being flooded by countless AI solutions
Every application is already equipped with or will be equipped with AI features. These different AI systems compete with and sometimes even hinder each other.
