Streamlining Performance & User Experience through Data
A powerful data and platform infrastructure is the backbone of the AI value chain. It ensures that data can be efficiently collected, processed, analysed and used to train and optimise AI models.
Data often comes from different systems, is incomplete or inconsistent. Managing large and complex amounts of data is also resource-intensive and time-consuming. In addition, the protection of sensitive data and compliance with legal regulations require special attention. A clear AI data strategy helps to master these challenges efficiently.
Automation and increased efficiency: AI-supported systems automate data integration, preparation and analysis, which increases efficiency and reduces errors.
Quality control: AI platforms can continuously monitor data quality and ensure that only reliable data is used.
Scalable solutions: AI-based platforms are flexible and scalable to keep pace with growing data volumes and changing requirements.
Security and compliance: AI-based security solutions monitor data activity and detect potential threats in real time, increasing data security.
Resource optimisation: AI reduces the need for manual intervention and allows employees to focus on value-adding tasks.
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AI Data & Platform Suite
Personalization at Scale
The ability of AI to analyse large amounts of data in real time and create personalized content from it has taken personalization to a new level. AI enables companies to precisely understand user behavior and preferences and offer them tailored experiences. This in-depth personalization leads to greater customer satisfaction, higher conversion rates and long-term customer loyalty. By using AI, companies can implement scalable, personalized marketing strategies that go far beyond traditional approaches.
Conversational Interfaces
AI-driven conversational interfaces such as chatbots and voice assistants have revolutionized the interaction between brands and customers. These interfaces offer round-the-clock support and improve the customer experience by providing fast, accurate and personalized responses. AI makes it possible to use natural language processing and machine learning to make conversations more fluid and human-like. The impact of AI in this area can be seen in higher customer satisfaction, lower support costs and increased efficiency of customer service teams.
MarTech Stacks
In MarTech Stacks, AI has changed the way companies plan and deploy their marketing technologies and strategies. AI helps to integrate data from different sources, recognize patterns and make precise predictions. This allows target groups to be addressed in a more targeted manner and marketing budgets to be used more efficiently. By using AI, companies can react more quickly to market changes, develop innovative campaigns and strengthen their competitiveness. The current impact of AI in MarTech stacks is enormous and offers companies the opportunity to fundamentally transform their marketing processes.
Data & AI Foundation
The Data & AI Foundation is the backbone of any successful AI initiative. The impact of AI today is demonstrated by the ability to efficiently manage and analyze vast amounts of data and turn it into valuable insights. A solid data and AI foundation enables companies to make data-driven decisions and develop innovative AI solutions that support their business goals. AI ensures higher data quality, improves data security and enables the seamless integration of different data sources. This enables companies to react faster and more precisely to market changes and achieve sustainable competitive advantages.
Artificial intelligence is finally making personalisation at scale possible. People have different expectations of content. In the future, thanks to customer data, it will be possible to generate personalised content that adapts precisely to these different expectations
Chief Transformation Officer
Plan.Net Group
Beyond Boundaries – what makes us unique as AI agency
With our experienced team of experts and a holistic approach, we offer innovative solutions that are customised to your specific business requirements. We use the latest technologies to optimise your data strategy, increase your efficiency and enable better decisions based on data and facts.
We provide you with long-term support on your path to digital transformation, ensure the highest data quality and security and offer flexible, scalable solutions that grow with your company. Your success is our focus – together we will unlock the full potential of AI and create sustainable added value for your business.
Our expertise in dealing with AI tools and technologies within the House of Communication is versatile and goes far beyond the generation and playout of content. We are convinced that AI can only realise its full potential if it can be seamlessly integrated along the entire value chain. This means seamlessly linking strategy, creation and technology as well as implementing the necessary governance structures.
You will find further answers in our FAQs
Any questions left?
The decision to set up your own GPT/LLM depends on several factors:
1. Resources: Building and maintaining your own model requires considerable resources in the form of computing power, expertise and time.
2. Use case: If there are special requirements or special data that require a customised model, it may make sense to have your own LLM.
3. Costs: The costs of setting up and maintaining your own model are high. Companies should weigh up the costs against the benefits.
4. Data protection: In-house models offer more control over the data and its processing and enable customised solutions for specific use cases.
On this basis, companies should consider whether their own GPT/LLM is worthwhile. The use of existing, well-trained models from third-party providers may well be more cost-efficient and quicker for companies to implement.
The introduction of LLMs can also be worthwhile for small and medium-sized companies, especially if the aim is to automate repetitive tasks, improve customer service or support data-based decisions. A dedicated model for specific use cases can be particularly useful in niche markets. However, companies should carefully consider the costs and effort involved in implementation and maintenance and may need to rely on cost-effective, scalable solutions from third-party providers.
AI chatbots can be used safely to advise customers if certain aspects are taken into account:
1. Data security: it must be ensured that the chatbot platform adheres to high security standards.
2. Transparency: customers should be informed that they are communicating with an AI chatbot
3. Data protection: Sensitive customer data must not be processed via the chatbot or must be protected accordingly. Providers should comply with EU law. US law for example, has more relaxed data protection guidelines that are not sufficient for the German market.
4. Quality control: Regular monitoring and updating of the chatbot is necessary to minimise misunderstandings and errors.
Data preparation involves cleaning, transforming and structuring raw data in order to make it usable for analysis or modelling. Consistent data preparation is necessary for the following reasons:
1. Quality of results: Clean and well-structured data leads to more accurate and reliable AI models.
2. Efficiency: Well-prepared data accelerates the analysis process and reduces computing time.
3. Error prevention: High quality data reduces the risk of errors and bias in the results.
Whether data is stored locally or in the cloud depends on several factors:
1. Security: the cloud often offers robust security mechanisms. Local storage, on the other hand, can offer more control.
2. Accessibility: cloud storage allows easy access from anywhere and supports collaboration
3. Cost: Cloud services are often more cost-efficient and scalable
4. Compliance: Certain legal regulations may require local storage.
