Engenai’s generative AI search intelligence is powered by vector databases and Large Language Models (LLMs). We help your customers discover the breadth and depth of your inventory to drive revenue. We unlock the full potential of your enterprise data, while protecting your competitive advantage. Engenai minimizes your initial capital investment while accelerating your time to market by allowing you to manage and control the usage and cost of multiple LLM implementations from a single dashboard.
Can you tell us a little bit about your role and background?
My name is Sergio Gonzales, and I am responsible for overseeing the company’s strategic growth by driving revenue-centered business cases for our technology and acquiring customers who can immediately benefit from our technology. I have more than 20 years of expertise in technology and online commerce through my roles at Accenture and as the former Director of Innovation Programs at eBay. Outside of Engenai, my work focuses on creating equitable economic opportunity through empowering practical, actionable civic innovation.
Why were you inspired to build Engenai?
It was important to me to democratize access to enterprise generative AI, a technology that will profoundly affect almost every aspect of our lives. As with any transformative technology, generative AI unlocks our imagination of what is possible. But after the initial buzz and attention fades, it falls on companies to figure out how this exciting new technology can drive real results for their business. If you aren’t resourced to build a core AI competency within your business or if you aren’t ready to invest millions in AI infrastructure, how do you get started? That’s where Engenai comes in. We operationalize the Large Language Models (LLM) that power generative AI and create a bridge for companies, large and small, to leverage AI for their business.
What does generative AI enable that wasn’t possible before?
It allows us to interact with information in a way we’ve never been able to before. We don’t currently interact with information the way we naturally think. We’ve learned to navigate information based on the limitations of how it has been restrictively categorized and structured. We’ve learned to search for things the way we think that a search algorithm will understand us. We use SKUs and model names to look for products and that’s fine if we know exactly what we’re looking for. But it fails us when we are exploring and looking for inspiration and new products to discover. We use stunted phrases and keywords in our web searches because that’s easier for the search engines to understand. Because everyone else is doing the same, it exponentially reinforces and trains the algorithm to only respond to this phrasing. We receive irrelevant results, or we miss out on better results because we lose the nuances of our language and grammar. We compromise our intention with these workarounds.
Now, imagine our world with generative AI where we look for camping gear based on the destination or weather conditions. Imagine being able to find the perfect birthday present because we can describe it based on our loved ones’ personalities and interests. Imagine having a conversation with a search engine where the millions of results are continuously refined, bringing you closer to the exact information you are looking for. That is the power of generative AI.
What is the key competitive business advantage of Engenai?
Flexibility, cost management, and time to market. The Engenai platform allows businesses to use multiple LLMs across proprietary, open-source, and custom models. This gives companies the flexibility to choose the right model for the right purpose, without incurring the heavy cost of technology lock-in. Operations are managed through one central management console, providing transparency and discoverability of the ongoing LLM utilization and costs. Instead of having to invest in several engineers and millions in expensive GPU infrastructure, businesses can leverage Engenai’s turnkey solutions to go live in weeks instead of months.
What’s next for Engenai?
Our first offerings are focused on bringing search intelligence to retail, commerce, and enterprise. Today, more than $300B in sales are lost each year in the US alone because customers can’t find what they’re looking for. Add losses from smaller cart sizes, missed customer acquisition, and customer attrition, and a company could be missing out on almost 5% in direct and indirect revenue. This is equivalent to the projected average sales growth rate for the retail industry. In essence, you could double your sales growth just by fixing your zero, low, and irrelevant (ZLI) search results.
At the enterprise level, it is difficult for companies to leverage the full potential of their enterprise data because accessing that data requires a deep understanding of the specific data structures. Asking a seemingly simple question like, “show me my most valuable customers, between the ages of 35 and 45, in the northwest region,” would likely need help from a data analyst who can form that query and run the report for you. What do you do when you need information urgently about an emerging supply chain issue like, “show me inventory shipments that are delayed by more than one week”? Do you have the time and resources to create that new query? What about the next one? This is where generative AI that can categorise and filter complex data using natural language processing can help.
On the technology front, new Large Language Models are developing at break-neck speed, and we are focused on integrating the best-of-breed models so that our clients have access to the models that will drive revenue for their business. We are excited to be partnering with the early leaders in this space and are adding new options for our customers every day. What this means for our customers is that as new leaders emerge in the market, they will automatically inherit those gains because they’ll be able to select and switch those models in their Engenai dashboard.
