CoSupport AI has introduced a cutting-edge architecture that moves away from conventional, one-size-fits-all solutions. Instead, it offers customized, scalable solutions that are more cost-efficient and capable of delivering faster, more accurate responses.
For CoSupport AI’s clients, this means their customer support systems will be able to provide quicker, more precise responses, resulting in improved customer satisfaction and operational efficiency. Let’s dive into detail how chatbots improve customer experience.
The New AI Architecture Pillars
CoSupport AI’s architecture is built on several key components that contribute to its innovative design. These components focus on additional security layers while offering more flexibility to the end users.
Vector Database
Vector database (DB) is the intermediary player that allows the system to effectively search for high-dimensional vector representations of data, and manage, and store them. When an AI model is trained with customized client data, it transforms into embeddings that are stored in a vector database. Thanks to vector similarity and indexing, these embeddings are easier to search and match.
As databases store client real-time and historical data, it's important for security measures to keep data inaccessible and secure. CoSupport AI's solution is to host each client's data separately, providing an additional layer of security.
Encoder Model
The encoder model transforms the initial inputs into embeddings that an AI model can process. While OpenAI's encoder model is more general and complex, CoSupport AI’s proprietary model is cheaper, faster, and more accurate. It offers significant cost and performance benefits thanks to its customized training data, efficiency in model size, and reduced complexity.
Large Language Models
In the majority of cases, the deployment of large language models can be done on the premises or in the cloud. CoSupport AI provides this flexibility to the clients, allowing them to deploy either on CoSupport AI's side or on their own infrastructure. This choice will define the further computing resources they need and the maintenance steps they follow.
Model's Context Extension (MCE)
With context extension, a model can use and retain more information to provide outputs. In short, the MCE defines the volume of text a model can consider, which consequently influences the output's efficiency. Such extension also helps a model to lead more complex and lengthy interactions.
CoSupport AI's custom approach leverages advanced graph technology and tools. This greatly improves AI's ability to understand and respond to complex user inquiries, increase resolution time, and enhance response accuracy. While providing coherent responses, the model tracks the history of previous dependencies, which leads to more efficient task completion.
New Architecture Advantages
From providing more customization to offering endless possibilities for scalability, CoSupport AI's new architecture has several advantages, enhancing various aspects of customer support.
Increased Response Speed
Due to the fact that CoSupport AI hosts the entire pipeline on the client’s own infrastructure, the chatbot can respond faster than traditional setups. The responses are received quicker thanks to reduced network latency and optimized resource allocation. In fact, the company creates its own configuration of the tool based on the client's demands and needs.
Improved Response Accuracy
With a custom-built router in the pipeline, the AI model gains a deeper understanding of customer inquiries, significantly improving response accuracy and reducing misunderstandings. The router forwards requests to the most suitable configuration, which allows processing with optimal resources.
Easier Scalability
The system supports the easy integration of new tools and capabilities, such as inventory checks or payment status inquiries, making it more scalable than previous architectures. Besides, there is no limit on the number of tools that can be integrated at the same time.
However, the model becomes dependable on the response time of the integrated tool. The more tools clients integrate, the more parameters they need to consider when it comes to performance.
Increased Cost Efficiency
Financial benefits of chatbots are evident and definite. If we look in detail, CoSupport AI's architecture, minimizes the need for large, complex system prompts, reducing token usage and operational costs, especially in large-scale projects. How does this happen?
- Reduced usage of tokens and prompts. Processing fewer prompts reduces the operational costs of a model.
- Enhanced context efficiency. The model limits the context window to actual inquiries. As a result, each task performance is more effective.
- Streamlined large-scale projects. The computational power is reduced. This is especially valuable in environments with numerous interactions.
- Faster responses. The model processes requests faster, consequently freeing up resources faster. This also means that the model can handle more requests at the same time, which is useful during peak seasons. Eventually, CoSupport’s chatbot helps increase customer engagement.
- Easier maintenance. The overall architecture doesn't rely on external guidance. Therefore, it’s simpler to maintain and update the model.
New CoSupport AI Architecture Use Cases
If we compare generalized and custom-tailored models for an AI-powered chatbot (CoSuppor Customer), we see clear advantages for the latter:
With reduced token usage, custom AI models optimize workflows. In such dynamic environments as customer support, this feature is especially valuable in enhancing response speed. Besides, while tailoring a model for specific needs, it handles complex requests more efficiently. However, what are the benefits of AI chatbots for customers? Why customized approach is crucial?
Its customized approach makes CoSupport AI's architecture beneficial for the tasks in customer support that require repetitive manual interventions and increased effort to maintain quick response rates. Also, implementing an AI tool with CoSupport AI's architecture can solve or improve the following challenges:
- Mediocre or low CSAT
- Disruptions in continuous customer support
- Long processing time
- Cumbersome manual processes
- Difficult scalability
- Integration challenges
- Low customer engagement
And for BI Tool (CoSupport BI), the model will work in the following way:
CoSupport AI is a better approach for handling large-scale datasets. As the model is flexible and scalable, it is well suited for such intensive tasks as detailed ticket analysis.
In addition, its customization allows it to better adapt to business needs and offer more efficient processing at lower costs with the usage of optimized resources. This is the best solution for companies seeking to automate and scale while staying within the defined budget.
Where is the BI solution the best fit? The easy scalability and flexibility of the tool make it stand out from the crowd for the following tasks:
- Company's expansion and automation of activities
- Strategic optimization of customer support tasks
- Prioritization focuses on high-quality leads
- Analysis of customer behavior and preferences
- Identification of upsell and cross-sell opportunities
- Increase in customer loyalty
Conclusion
CoSupport AI's new architecture represents a major leap forward in AI-powered customer support. By leveraging custom components such as the Vector DB, proprietary Encoder Model, and Model’s Context Extension, it provides faster, more accurate, and scalable solutions compared to conventional approaches.
The architecture’s ability to integrate advanced tools, reduce operational costs, and improve overall response quality makes it an ideal choice for companies looking to enhance their customer support efficiency.
With this innovative approach, CoSupport AI ensures businesses can deliver exceptional support experiences, driving greater customer satisfaction and efficiency.
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