Discover some examples of Generative AI Use Cases and what how you can level up your organization and business
In the dynamic landscape of artificial intelligence, Generative AI agents have taken the center stage when it comes to adding value to organizations' processes. At DareData Engineering, we believe in a human-centric approach, where AI agents work together with humans to achieve faster and more efficient results. Additionally, we believe that there's a significant layer of machine learning expertise required to improve GenAI agents performance and ability to discover intents / perform tasks. In this blog post, we'll delve into some of our project portfolio in the Generative AI space and understand how we are deploying GenAI at our customers. Jump in!
GenAI in Action
QA Forum Chatbot
Our journey in Natural Language Processing started way back in 2019, working with traditional Text Mining and our journey with GenAI agents began with the development of a chatbot tailored for the telecommunications sector. Powered by GPT 3.5 Turbo, our chatbot represents a milestone in commercial generative AI. Positioned as the first commercial application of its kind within NOS, this chatbot serves as a gateway for customers to access information about NOS services seamlessly.
Key to its functionality is the implementation of Retrieval Augmented Generation (RAG) techniques, enabling the chatbot to sift through vast corpora of articles with precision. By extracting pertinent information, it delivers contextually appropriate responses to user queries, redefining the landscape of customer support and engagement.
Retrieval Augmented Generation (RAG) stands as contemporary artificial intelligence technique. At its core, RAG harnesses the power of large language models and vector databases to augment pre-trained models (such as GPT 3.5). This approach enables AI systems to sift through vast repositories of unstructured data with precision, extracting contextually relevant information to generate responses that are accurate but also imbued with nuance and depth.
Most vanilla RAG implementations are difficult to manage and implement due to the non-structured format. Only be mastering natural language processing, machine learning and other NLP techniques can one harness the full power of RAG implementations (you'll particularly deal with troublesome RAG implementations if you try out-of-shelf GPTs, where bots are unable to answer questions clearly and accurately). Below, you can see an example of how RAG architecture works:
RAG Methodology Step-by-Step:
This GenAI Agent enabled quicker access to NOS Forum articles, saving users a few clicks and deep searches on the forum threads, improving customer satisfaction. Curious? Try it for yourself!
GenAI SQL Agent - B2B Sales
While sales traditionally thrives on the human touch, characterized by personalized interactions and the artistry of communication, the notion of integrating custom agents into this domain is not only feasible but also highly advantageous. Despite its inherently human-centric nature, the sales arena stands to gain immensely from the incorporation of tailored AI agents.
This project serves as a bridge between two historically distinct realms: coding and sales. By leveraging the capabilities of Large Language Models (LLMs), Natural Language Processing (NLP), and SQL technology, our solution facilitates the communication between sales professionals and databases. Empowering sales consultants and assistants to converse in the language of SQL, it enables swift access to critical client information. Representing an example of human-centric AI implementation, this project empowers sales people with new abilities to become more efficient in the sales process.
At its core lies a sophisticated chatbot capable of translating user inquiries into SQL queries. Our GenAI SQL Agent opens new avenues for client engagement and revenue generation.
The success and applicability of this approach extend beyond the confines of a singular project, offering a blueprint for integration across diverse languages and systems. While the traditional essence of sales leans on personal interactions and communication, the strategic incorporation of tailored AI agents proves not only viable but also profoundly beneficial
GenAI CoPilot
In the realm of call centers, the AI Copilot project transformed operational efficiency. Designed to assist contact managers in reducing mean call time and enhancing productivity, this AI-powered copilot leverages LLMs, NLP, and Vector Databases to streamline workflow.
Through the implementation of Retrieval Augmented Generation (RAG), the copilot quickly retrieves information from a vast knowledge repository, providing contextually relevant responses to customer inquiries. Empowering contact managers with advanced tools, our GenAI CoPilot revolutionizes customer interactions, paving the way for a human-centric approach to AI deployment. The need for a swift deployment was only done possible by a clever engineering effort that leverages DareData's experience in Data Engineering and knowledge of the MLOps world. An example of the architecture we've deployed at this project is the following:
In a call center setting, the urgency of accessing information from diverse sources cannot be overstated. To address this challenge, our architecture prioritizes speed and efficiency, integrating different data streams to provide contact managers with insights at a moment's notice. This is only possible with well designed architectures and systems. By fusing GenAI technology with sound engineering principles, the GenAI CoPilot project stands as a testament to the critical role of good engineering in driving innovation and success in AI deployment, something that we personally believe, at DareData.
Embracing the Future
As we continue to push the boundaries of innovation with our GenAI solutions, DareData Engineering remains committed to shaping a brighter deployment of GenAI solutions. From knowledge bases to B2B sales, our projects exemplify the transformative potential of artificial intelligence when coupled with human expertise.
Check out the landscape of use cases we've implemented so far:
Interested in getting to know our GenAI Agent solutions? Contact me ivo@daredata.engineering or through our website.