The use of AI for process optimisation and improving efficiency has long been indispensable.
So why opt for customised AI rather than off-the-shelf solutions?
We develop and implement company-specific AI solutions that provide support exactly where your business needs it and are perfectly tailored to your needs, requirements and guidelines: from generative AI (Large Language Models and GPT models) to autonomously operating AI agents.
Enterprise-wide AI solutions for process automation and specialist solutions for departments such as IT, HR, back office and many more.
Productive in no time: from the initial idea to the finished end-to-end solution
Secure and scalable AI solutions that integrate seamlessly into your infrastructure
The collaboration with novaCapta was focused, professional, and truly collaborative. Bonnie is an important step in our digital evolution – clear, secure, and future-ready.
The collaboration with novaCapta was professional and collaborative from the start. Together, we were able to successfully implement the Copilot pilot project and gain valuable insights for our further AI strategy.
Whether you have a specific problem or are just looking for some initial guidance, we start by analysing your processes together and identifying the AI use cases that offer the greatest potential for your business.
In the context of artificial intelligence, agents are regarded as software entities that perform specific tasks, interact with their environment and other agents, and are capable of acting autonomously – that is, taking independent action to achieve predefined goals.
AI agents have the following characteristics:
The development of AI agents opens up new possibilities for automated, intelligent processes within organisations.
Agents can interact with other systems, applications and devices in a networked environment via APIs (Application Programming Interfaces).
Examples and use cases for (autonomous) AI agents in business and industry include, for example:
Process automation: AI agents can, for example, be used to control production. They can optimise the flow of materials by predicting delivery times and automating stock management.
Predictive Maintenance: AI agents can be used to predict when machines will require maintenance. By analysing operational data, they can identify when certain parts are likely to fail, thereby enabling them to plan preventive maintenance and automatically order spare parts where necessary.
Customer service: AI agents can be used as chatbots in customer service. They can answer customer enquiries in real time, provide information about products, take orders and arrange dispatch.
Healthcare: AI agents can assist healthcare professionals in diagnosing illnesses, analysing medical images, predicting patient outcomes and personalising treatment plans. This improves accuracy, efficiency and patient care. At novaCapta, we specialise in developing apps as medical devices and are certified to ISO 13485.
Quality control: AI-driven image recognition systems can be used on the production line to check products for defects. For example, they can inspect surfaces for scratches or irregularities, thereby improving quality assurance.
Data analysis: AI agents can be used to analyse manufacturing data by identifying patterns in production data that may indicate quality issues, thereby enabling early corrective action. In this way, they can help to increase revenue.
Supply Chain Optimisation: AI agents can optimise supply chain processes by forecasting demand, optimising stock levels and reordering materials, improving logistics routes and identifying potential cost savings.
Energy management: In large industrial plants, AI agents can monitor and control energy consumption. For example, they can improve energy efficiency by adjusting the operation of machinery to actual demand and switching it off or on again as required.
Business travel: AI agents can assist with planning and booking by analysing employee and customer preferences, past travel behaviour and company policies. They can recommend suitable accommodation and transport options based on factors such as cost, comfort and journey time. Furthermore, they can handle the booking process, make reservations and manage itineraries, and provide personalised recommendations – for example, for restaurants at the destination – based on the travellers’ preferences.
AI assistants (Gen AI) and AI agents are two different forms of artificial intelligence.
Whilst AI agents act autonomously, AI assistants offer a personalised service.
When several AI agents work together, this is referred to as a multi-AI agent system (or a ‘multi-agent system’). Several so-called ‘collaborative AI agents’ can thus tackle even more complex tasks.
Companies that use a ‘multi-AI agent system’ benefit from the scalability of AI solutions and, consequently, increased efficiency.
GPT (the abbreviation GPT stands for Generative Pre-Trained Transformer), developed by Google in 2017 and further refined by OpenAI for a wide range of purposes, is a family of AI models based on the Transformer architecture and representing state-of-the-art models for data processing. What the GPT models have in common is that they have been ‘pre-trained’, i.e. they are trained on large amounts of data – such as text, images, videos, etc. – before being fine-tuned for specific tasks.
The best-known GPT model is arguably ‘ChatGPT’, developed by OpenAI, a leading company in the field of AI research, and first unveiled to the public in November 2022. ChatGPT functions like a chatbot and can respond to questions or queries by using machine learning to understand the context of the conversation and generate appropriate answers. To this end, ChatGPT was trained on a vast amount of data from the internet, including books, articles, websites and social media, as well as on the understanding of human language.
For example, the responses generated by the GPT-3.5 version were evaluated by several thousand test subjects in order to improve the accuracy and quality of the responses from a moral and ethical perspective. The next iteration, GPT-4, utilises even more data and greater computing power to answer increasingly complex queries. Thanks to its broader general knowledge and problem-solving abilities, GPT-4 can solve difficult problems with greater accuracy. It can also be more creative and collaborative than its predecessors by working alongside users on creative and technical writing tasks and, for example, learning a user’s writing style.
The text-based use of GPT is the best-known application scenario to date and can be utilised in a variety of ways within organisations: from virtual assistants and chatbots with speech and text recognition, through text generation, summarising complex texts from emails, financial reports and relevant news (press reviews), as well as contract assessments, right through to automatic data classification and distribution.
However, GPT models can be used for a wide range of more specific tasks, particularly in an industrial context.
Integrating GPT models into your Microsoft 365 and Azure environment using Azure OpenAI enables a dynamic convergence of artificial intelligence and comprehensive productivity and cloud computing platforms. This integration allows your users to harness the capabilities of GPT within the familiar Microsoft ecosystem.
Azure OpenAI is a service developed jointly by Microsoft and OpenAI, offering advanced AI models such as GPT-3, Codex and DALL-E.
Specific examples of APIs include:
GPT is particularly well-suited to building on existing structures. GPT uses the data and documents within the ecosystem (e.g. Microsoft, SAP) as the basis for its responses. In turn, GPT can be used to further optimise the process of finding information, for example within the Microsoft environment. Both technologies benefit from each other, thereby providing users with the best possible experience.
Tell us about your current situation, and we’ll get back to you with an initial assessment of potential AI use cases for your business.
Alexander Elkin
Head of Applications & Data
