HOW DOES A KI PROJECT WORK?
An AI development project is a complex endeavour that goes through several targeted phases to ensure that the finished application meets the requirements and works reliably. Here is an overview of the typical process of such a project:
1. initiation: in this initial phase, the foundation for the AI project is laid. Goals are defined, the company's requirements are assessed and an initial project plan is drawn up. It is important in this phase to create a clear understanding of how the AI should support the business objectives and to ensure strong commitment from the entire team.
2. conceptualisation: Once the initial information has been collected, the conceptualisation phase begins. This is where the fundamental design of the AI solution is developed. This includes the selection of suitable algorithms, the definition of data structures and the planning of interfaces. A detailed project plan is drawn up that includes milestones, resources and schedules. Prototypes are also designed in this phase in order to gain initial insights into the potential functions of the AI.
3. realisation: The AI application is developed in the realisation phase. The previously created design is implemented, codes are written and learning algorithms are trained. Iterative development processes play a major role here in order to be able to make improvements based on continuous feedback.
4. QA and testing: Quality assurance and testing are crucial to ensure a functional and reliable AI application. During this phase, various test methods such as unit tests, integration tests and system tests are carried out. Bugs are fixed and the performance of the AI is carefully evaluated. This phase is crucial to guarantee the usability and accuracy of the AI.
5. go live: After successful quality assurance and final testing, the AI application goes live. At this stage, the application is used in the real environment and made available to end users. The live transition often also includes the migration of data and integration into existing systems.
6. service & support The service and support process is of ongoing importance after the application has gone live. In this phase, continuous maintenance, troubleshooting and optimisation are offered. Support ensures smooth operation and helps to further develop the AI solution so that it can keep pace with changing business requirements or technological innovations.
Going through these phases ensures a structured approach and enables the successful implementation of an AI solution that creates real value for the organisation.
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CAN THE aI BE USED IN MY COMPANY?
We can provide solutions that are tailored to your organisation's exact needs and goals, whether that's automating routine tasks, improving customer interactions or supporting data-driven decision-making. Our team would love to work with you to explore the possibilities and develop a customised implementation plan to help you reap the full benefits of AI. Let's arrange a meeting to discuss your specific requirements and find the best way to integrate AI into your business processes.
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WHAT MAKES US A AI AGENCY?
Are you looking for a competent AI agency that develops customised AI solutions and meets your needs? Then we are the right choice for you. Here are some convincing reasons why we stand out as your AI partner:
Years of experience and expertise:
We are a team that has gained extensive experience in the AI industry over many years. Specialising in individual software projects that use AI, we have successfully implemented a variety of projects. Our portfolio ranges from small, customised solutions to complex and extensive AI applications for medium-sized companies and corporations. This experience enables us to offer you tried-and-tested AI solutions that are tailored to your specific requirements.
Interdisciplinary team of experts:
Our team of experts consists of professional engineers for engineering prompts, creative UX designers, savvy software specialists, clever information architects and strategic AI consultants. This interdisciplinary composition allows us to develop innovative and user-friendly AI solutions that can be perfectly integrated into your business processes.
Our own technology platform nele.ai:
With nele.ai, we have built our own technology platform that allows us to operate your AI applications securely and efficiently.
Our technology guarantees the responsible use of AI and ensures compliance with the highest standards of data protection and data security.
Customised AI solutions:Our approach is not just to develop AI models, but to deliver customised solutions that solve your business challenges. We understand that every business is unique and customise our AI applications to provide you with the greatest added value.
Focus on user experience:
As your AI agency, we place great emphasis on delivering the best user experience. Our UX designers are masters at creating intuitive and appealing user interfaces that transform the complexity of AI systems into ease of use for the user.
Future-orientation and innovation:
We stay at the forefront of technological progress and meet the challenges of the future with a spirit of innovation. Through continuous training and research, we ensure that our AI solutions are always at the cutting edge and give you a decisive competitive advantage.
We are more than just an AI agency. We are your strategic partner who will navigate you through the complex world of artificial intelligence and help you to fully utilise the potential of this ground-breaking technology. Trust our expertise and passion for AI. Contact us today to discover what we can do for your organisation.
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WHAT DOES ARTIFICIAL INTELLIGENCE COST?
The price of artificial intelligence depends heavily on the requirements. Overall, the pricing process is made up of the models used as well as the interfaces, which have to be customised and developed. If you want to use artificial intelligence so that it can be used universally, such as nele.ai, then the costs are between ten and 500 € for the entire company, depending on the volume of use. The costs for customised development can only be defined after a workshop and are usually in the low four-digit or mid five-digit range.
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WHICH AI MODELS ARE AVAILABLE?
Artificial intelligence (AI) is a very broad and interdisciplinary field that encompasses many different models and approaches that can vary depending on the application and requirements. Here are some common categories of AI models and some examples:
1. machine learning (ML):
- Supervised Learning: Classification (e.g. Support Vector Machines, Decision Trees, Random Forests, Naive Bayes, k-Nearest Neighbours), Regression (e.g. Linear Regression, Lasso, Ridge Regression).
- Unsupervised learning: Clustering (e.g. k-Means, Hierarchical Clustering, DBSCAN), Dimensionality Reduction (e.g. Principal Component Analysis, t-Distributed Stochastic Neighbour Embedding).
- Reinforcement learning: Q-learning, Deep Q-Network (DQN), policy gradients, actor-critic models.
2. deep learning models (deep learning):
- Neural Networks: Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM).
- Generative models: Generative Adversarial Networks (GAN), Variational Autoencoders (VAE).
- Transformer models (Transformer models): BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), Transformer-XL, T5 (Text-to-Text Transfer Transformer).
3. expert systems:
- Rule-based systems: These use a collection of "if-then" rules to draw conclusions.
- Knowledge-based systems: These build on a complex knowledge database to deal with specific, often technical, problems.
4. symbolic AI:
- Logic-based systems: These use formal logic to draw conclusions (e.g. Prolog, First-Order Logic).
- Semantic networks: These represent knowledge in the form of relationships between different concepts.
5. evolutionary algorithms:
- Genetic algorithms: These mimic the process of natural selection to generate optimal or satisfactory solutions.
- Evolutionary strategies: A class of optimisation algorithms based on the imitation of natural evolutionary processes.
6. hybrid models:
- These combine different AI techniques to benefit from the strengths of each method and minimise their weaknesses.
7. neuro-fuzzy systems:
- These combine neural networks and fuzzy logic to process fuzzy information.
This list outlines some of the most common AI models, but the field is constantly evolving. New models and approaches are being developed to push the boundaries of current technologies and adapt them to specific challenges.
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HOW CAN AI SUPPORT COMPANIES?
Artificial intelligence (AI) can be used to support companies in many ways and offers a variety of applications that can optimise business processes, improve decisions and open up new opportunities. Here are some of the key areas where AI can support organisations:
1. automation of routine tasks: AI systems can automate time-consuming, repetitive tasks, such as data entry, scheduling and simple customer enquiries, leading to increased efficiency and cost savings.
2. data analysis and insights: AI algorithms are able to analyse large amounts of data and uncover patterns, trends and correlations that may not be visible to the human eye. This ability supports companies in making informed decisions.
3. personalised customer experiences: Companies use AI to provide personalised recommendations and content based on individual customer behaviour and preferences, which increases customer satisfaction and loyalty.
4. customer relationship management (CRM): AI can help improve a company's interactions with current and potential customers by providing insights into customer needs and helping to personalise communications and services
5. supply chain management and logistics: AI can make predictions about supply bottlenecks, fluctuations in demand or maintenance requirements and thus help companies to optimise their supply chains and logistics networks.
6. increasing productivity: tools that utilise AI can help to increase employee productivity, for example by enabling the optimal allocation of resources or the identification of inefficient processes.
7. business forecasting and modelling: AI models can be used to predict future sales patterns, improve financial modelling and manage business risks.
8. marketing and advertising: AI can help analyse consumer data to design effective marketing strategies and tailor advertising to specific target groups.
9. speech and image processing: AI functions such as natural language understanding, speech recognition and image recognition can improve the customer experience and be used, for example, in automated customer service systems or quality control.
10. security: In cybersecurity, AI can help to recognise anomalies, identify potential threats and respond to security incidents.
11. research and development (R&D): AI can accelerate innovative processes, e.g. by simulating new materials or analysing clinical trial data in the pharmaceutical industry.
12. talent management and acquisition: AI can be used in HR to identify suitable candidates, support personnel development and recommend personalised career paths.
AI is a versatile tool that can help companies optimise their processes, reduce costs, increase innovation and ultimately boost competitiveness. However, the successful use of AI also requires an understanding of how this technology can best be integrated and how ethical and data protection aspects can be taken into account.
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WHAT ARE THE RUNNING COSTS OF AI SOLUTIONS?
The running costs for AI solutions essentially comprise
Cloud service fees: Costs for storage and computing power in the cloud.
Licences and subscriptions: Monthly or annual fees for AI software or platforms.
Maintenance and updates: Regular costs for maintaining and updating AI systems.
Data management: Expenses for collecting, processing and managing data.
AI training: Costs for the continuous training and improvement of AI algorithms.
Specialist staff: Salaries and costs for specialists who support the AI systems.
Energy costs: Higher power consumption for computing-intensive AI applications.
System integration: Expenses for integrating AI into existing IT systems.
Support and customer service: Costs for technical support and customer service.
Compliance and security: Investments in data protection and security measures.
These costs are variable and should be assessed in the context of the specific business requirements and the expected benefits of AI.
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