See how RAG technology and AI advancements are revolutionizing sales, customer engagement, and business intelligence for real-time success
Joscha, would you mind sharing with us some insights into your professional journey and how you arrived at your current role as Head of Product at Connectly.ai?
My path to the tech industry and product management took a bit of an unconventional route. My introduction to product development started in the hair care sector, where I had the opportunity to dive deep into human needs and master the art of user-centric design. When I found myself looking for a more dynamic environment, I embarked on a nearly decade-long journey at Google.
I began in sales and gained invaluable insights into customer pain points and the intricacies of building relationships. This then laid the groundwork for my transition into a product role within the Ads organization at Google.
After my time at Google, I took a leap into the unknown and joined Connectly as the fourth employee—a decision fueled by the thrill of building something from the ground up.. Today, we have a global team of more than 50, we partner with category-defining customers, and we are pushing the boundaries of AI research and product development. I couldn’t be more excited about where we’re headed next.
How does RAG revolutionize customer interaction and business intelligence in sales, with a special emphasis on the critical aspects of accuracy and timeliness of information?
By combining a generative model with a retrieval system, Retrieval-Augmented Generation (RAG) enhances AI responses with accurate, current data.
Large Language Models (LLMs) in a production environment are constrained by their static datasets, and often lack in accuracy and timeliness. However, RAG introduces a dynamic component that leverages real-time external databases. This ensures that every piece of information it provides or action it recommends is grounded in the latest available data.
As the Head of Product at Connectly.ai, how do you foresee integrating RAG technology into your product offerings to enhance customer experiences and sales effectiveness?
RAG is one part of a cohesive AI strategy. At Connectly we also found that we had to start training our own embeddings as well as models to help make our AI Sales Assistant efficient, fast and reliable.
Traditional AI models often encounter challenges with stale data sets and complex queries. How does RAG address these limitations, and what advantages does it bring to AI systems in terms of improving responsiveness and relevance of information?
Complex queries that would stump earlier AI models are now within reach with enhanced query resolution. By employing sophisticated retrieval systems to gather data from numerous sources, RAG can dissect and respond to multifaceted questions in a nuanced way that was previously unachieveable.
Additionally, RAG has the capability to pull in and analyze data from diverse sources in real-time, which transforms it into a powerful tool for market analysis. This can then equip businesses and leaders with the agility to adapt to market shifts with insights derived from the most current data, offering a hard-to-match competitive edge.
Could you kindly elaborate on how Connectly.ai is leveraging RAG to enhance its AI sales assistants and provide more personalized and contextually relevant interactions for users?
Of course! RAG is one part of the AI sales assistant that we have built. Businesses share their product catalog with Connectly to inform our sales assistant. This product catalog can have many million products with different variants. The inventory and prices might change on a daily basis. In order to provide the end customer with real time and reliable data, we leverage RAG as part of our architecture.
In your esteemed experience, what key considerations or best practices should companies keep in mind when seeking to enhance their AI models with technologies like RAG to create better customer experiences?
I would recommend starting with a narrow use case first and learn from there. In our case we had to learn the hard way that, for example, offering a multi language product from the start came with many hurdles. Clothes sizing for example can be different from country to country. English makes up more than 40% of common crawl data, so language embeddings and foundational models will work better in English first.
What personal strategies or approaches do you employ to stay informed about emerging technologies and industry trends, particularly in the realm of AI and customer interaction?
There is so much happening and the AI industry is moving at a crazy pace. I have gathered a list of people I follow on X to stay up to date with some of the latest trends and discussions. I’m also lucky to be living in San Francisco where you will overhear a conversation about AI just about anywhere you go.
Drawing from your expertise, what valuable advice would you extend to our readers who are interested in implementing RAG or similar technologies to improve their own AI systems and customer interactions?
If you are incorporating AI into your business, I would always start with a design partner in mind who can provide you valuable feedback and insights and is willing to build with you. This can be an external stakeholder like a customer or an internal team. The external validation is extremely helpful and important to help solve actual problems and pain points.
As we come to the end of our discussion, would you be open to sharing any final thoughts or insights regarding the future of RAG technology and its implications for sales and customer engagement?
There is a lot of interesting discussion around the future of memory in AI. If a sales assistant can remember and learn from all previous conversations it had with a customer, it will evolve into a true personal shopper.
Finally, Joscha, could you provide us with some insight into what’s next for Connectly.ai and how RAG fits into your broader product roadmap for enhancing customer experiences?
We have a lot of exciting launches in the pipeline. We launched our sales assistant, Sofia AI, about 6 months ago and are already partnering with major global brands. One of the new features I am most excited about is our continued work on AI insights from the conversations customers are having with our sales assistant. These insights can be imported directly into a CRM and help our businesses truly understand their customers. Previously this would have only been possible by interviewing every member in the Sales staff.
Joscha Koepke
Head of Product at Connectly.ai
Joscha Koepke is Head of Product at Connectly. As part of the company’s founding team, he leads the product team in building and innovating its AI-powered conversational commerce platform, which enables businesses to operate the full flywheel – marketing, sales, transactions, customer experience – all within the customer’s thread of choice. Prior to Connectly, Joscha was a Global Product Lead for Google, leading the product & go-to-market strategy of emerging online-to-offline ad format products across Search, Display, YouTube, & Google Maps.
