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Implementing AI Solutions: Secure, Trusted, and Customized for Your Business
DALLAS, TX; November 4, 2024
Implementing AI Solutions: Secure, Trusted, and Customized for Your Business
Our Gen AI Retrieval-Augmented Generation (RAG) based chatbot is not just a tool—it is a strategic asset that enhances customer satisfaction, drives engagement, and boosts operational efficiency. RAG is a popular AI framework that combines generative large language models (LLMs) with traditional information retrieval systems. RAG can help generate more accurate, relevant, and up-to-date text by optimizing the output of an LLM to reference an authoritative knowledge base outside of its training data sources.
Advantages of a GenAI Chatbot:
- 24/7 availability: Provides real-time answers to commonly asked question around the clock
- Cost Optimization: Minimizes the need for human intervention
- Efficiency: State-of-the-art query throughput and latency
- Consistency: Offers consistent responses, reduces human error and ensures that all customers receive the same level of service
- Data Collection and Analysis: Provides insights into customer behavior and pain points which can be used to improve business processes
- Scalability: Scales easily with Kubernetes, ensuring that customer service levels remain high even during peak times
At WorldLink, we follow a structured approach to building AI solutions to ensure that the solution aligns with your goals, values and objectives. This process ensures delivery of a solution that is customized for your specific needs.
The first step, before diving into development, is to understand the chatbot’s mission. What customer pain points will the chatbot address? Who is the target audience? Defining clear objectives will ensure the chatbot delivers real value and saves time for the customers. The next step is to select the right architecture. We ensure that the selected tools and technologies align with the needs of the organization and use cases, as seen in the sample architecture below:
Key components of the architecture are as follows:
1. Embedding Model:
An embedding model is a type of machine learning model that transforms data into numerical vectors, or “embeddings.” These vectors capture the meaning or characteristics of the data in a way that makes it easier for computers to work with.
2. VectorDB:
This is a specialized database designed to store, manage, and query data represented as high-dimensional vectors. Key factors to consider while choosing a VectorDB are query performance, search and retrieval features, and data handling capabilities.
Popular options to consider include Milvus, Chroma, Pgvector (PostgreSQL extension) and Faiss.
3. Open Source LLMs (e.g., Llama 3, Mixtral):
These language models are the linguistic geniuses behind your chatbot, understanding and generating human-like text with uncanny precision, trained on vast amounts of text data to understand and generate human-like language.
Llama 3 70b and Mixtral 8x7b are some of the best open source LLMs.
After identifying the problem and finalizing the architecture, the next step is to begin development. Development consists of the following steps:
While timelines could vary depending on the specific use case, a typical timeline to implement Minimum Viable Product (MVP) using open-source AI would be as follows:
- Week 1-2: Initial Set up
- Week 3-7: Implement RAG framework
- Week 8-10: Build and Deploy the Chat Interface
- Week 11-15: Testing and Refinement
- Week 16 onwards: Go-Live and Continuous Improvement
Join the ranks of leading businesses that are transforming customer service with intelligent automation. Experience the future of customer engagement with our Gen AI RAG-based chatbot and enhance customer engagement, satisfaction, and stand out in the competitive market. Here’s another of our customer success story and their AI journey with us: Case Study – Empowering Operational Efficiency with GenAI.
Request a demo today and discover how it can revolutionize your business and upscale customer interactions with our advanced AI technology. Come innovate with us.
Written by Priyanka Hosabettu, Product Manager at WorldLink
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