I’m thrilled to sit down with Dominic Jainy, an IT professional whose deep expertise in artificial intelligence, machine learning, and blockchain has positioned him as a thought leader in applying cutting-edge tech to real-world challenges. Today, we’re diving into the fascinating world of retail innovation, specifically focusing on a groundbreaking AI tool called Ellis, developed by First Insight. In this conversation, Dominic shares his insights on how predictive AI is reshaping the retail landscape, the unique capabilities of domain-specific models, and the transformative potential for retailers navigating fast-paced markets and evolving consumer behaviors.
Can you tell us what sparked the creation of a tool like Ellis, and what gap in the retail industry it aims to fill?
The inspiration behind a tool like Ellis comes from the urgent need for retailers to keep pace with rapidly changing consumer trends while making data-driven decisions. Retail has always been a high-stakes industry with thin margins and unpredictable demand. The idea was to build an AI copilot that doesn’t just analyze data but predicts trends months ahead, helping retailers stay proactive rather than reactive. Ellis fills the gap of actionable intelligence—turning raw consumer insights into concrete strategies for planning, pricing, and product launches.
How does Ellis differentiate itself from other AI tools that retailers might already have in their toolkit?
What sets Ellis apart is its focus as a domain-specific AI built exclusively for retail. Unlike general-purpose AI tools that pull from broad web data and often lack context for nuanced retail challenges, Ellis is tailored to understand the intricacies of this industry. It’s not just about crunching numbers; it’s about delivering predictive insights that are directly relevant to apparel, grocery, or home goods. This specialization makes its recommendations far more precise and actionable compared to generic models.
What are some of the biggest pain points in retail that Ellis is designed to tackle?
Retailers often struggle with long trend-to-market cycles, volatile consumer demand, and the complexity of juggling pricing, inventory, and assortment decisions. Ellis addresses these by providing predictive insights that shorten decision-making timelines and reduce guesswork. For instance, it helps identify what products will resonate with customers before they hit the shelves, ensuring retailers aren’t stuck with unsold inventory or missing out on hot trends. It’s about minimizing risk and maximizing opportunity in a very competitive space.
Can you walk us through how Ellis uses its predictive capabilities to forecast shopping trends well in advance?
Ellis leverages a retail-specific large language model (LLM) that’s been trained on vast amounts of historical and real-time data from the retail sector. It analyzes patterns in consumer behavior, market shifts, and product performance to spot emerging trends months before they become obvious. By combining predictive algorithms with natural language processing, it can simulate future scenarios and offer retailers a clear picture of what’s likely to happen—whether it’s a surge in demand for sustainable fashion or a shift in holiday shopping habits.
How does the training data for a retail-specific LLM like Ellis differ from that of more general AI models?
General AI models are often trained on publicly available web content, which is incredibly broad but lacks depth in specific industries. Ellis, on the other hand, is trained on curated datasets that include retail-specific information—think consumer purchase histories, product category trends, and even seasonal buying patterns across sectors like apparel or home goods. This focused training allows it to understand retail nuances, like the impact of a price change on demand, in a way that generic models simply can’t replicate.
What types of data does Ellis pull from to generate its insights for retailers?
Ellis dives into a wide range of data points, primarily consumer and product-related information. This includes shopping behaviors, preferences, and feedback from customers across various categories. It also looks at product performance data—sales figures, inventory turnover, and even social sentiment around certain items. By blending these datasets, Ellis can paint a comprehensive picture of what’s driving demand and where opportunities lie for retailers to capitalize on.
How does Ellis make natural language interaction valuable for retail teams, and can you share an example of how they might use it?
The beauty of Ellis is its conversational interface, which lets retail teams ask questions in plain language without needing to be data scientists. Instead of sifting through complex dashboards, a team member might simply ask, “What styles of jackets are likely to trend this winter for Gen Z shoppers?” Ellis would respond with predictive insights based on current data, helping the team make informed decisions quickly. It’s like having a strategic advisor on hand, but powered by AI.
Can you explain how Ellis supports retailers in assortment planning, one of the key areas it’s being tested on?
Assortment planning is all about deciding which products to stock and in what quantities, and Ellis streamlines this by predicting what will sell best in specific markets or demographics. It analyzes past sales, current trends, and consumer feedback to recommend a balanced mix of products. For example, it might suggest focusing on eco-friendly apparel for a particular region based on rising demand signals, ensuring retailers avoid overstocking items that won’t move.
How does Ellis assist with pricing decisions, and what builds trust in its recommendations?
Pricing is a delicate balance, and Ellis helps by modeling how different price points might impact demand and profitability. It looks at competitor pricing, consumer willingness to pay, and historical sales data to suggest optimal thresholds. The trust comes from its retail-specific training—it’s not guessing based on generic patterns but drawing from real-world retail outcomes. Retailers can see the data backing each recommendation, which builds confidence in applying those insights.
First Insight claims Ellis can cut trend-to-market cycles dramatically. How does it achieve this, and what’s the impact on retailers?
By using predictive AI, Ellis compresses the traditional nine-month trend-to-market timeline down to as little as four weeks. It does this by identifying trends early through data analysis and providing actionable steps immediately, so retailers don’t waste time on trial and error. For retailers, this means faster launches, fresher inventory aligned with current demand, and a competitive edge in a market where speed is everything. It’s a game-changer for staying relevant.
With the holiday season around the corner, how are pilot companies leveraging Ellis, and what feedback have you seen so far?
Pilot companies are using Ellis to fine-tune their holiday strategies—everything from stocking the right products to setting competitive prices. They’re asking it to predict high-demand items for the season and optimize inventory to avoid shortages or overstock. Early feedback has been promising; many are impressed by how quickly Ellis turns complex data into clear, usable advice, especially under the pressure of holiday timelines. It’s helping them feel more prepared for peak shopping periods.
Why does a retail-specific LLM like Ellis outperform general models when addressing the unique challenges of this industry?
General LLMs, trained on broad internet data, often miss the mark on retail’s specific needs because they lack context about things like seasonal buying cycles or category-specific trends. Ellis, being purpose-built for retail, understands these nuances inherently. It’s not just processing words; it’s interpreting data through a retail lens, which means its insights are more relevant and directly tied to measurable outcomes like sales or inventory turnover.
How does Ellis transform consumer feedback into practical decisions for retailers, and can you share a specific scenario?
Ellis takes raw consumer feedback—whether it’s survey responses or social media sentiment—and translates it into strategic moves. For instance, if feedback shows growing frustration with high shipping costs, Ellis might recommend bundling products or offering free shipping thresholds to boost conversions. It connects the dots between what customers are saying and what retailers can do, ensuring decisions aren’t based on gut feelings but on solid evidence.
Looking to the future, what does it mean for Ellis to evolve into a ‘system of outcome,’ and how will it integrate into a retailer’s broader operations?
The concept of a ‘system of outcome’ means Ellis won’t just provide insights but will orchestrate decisions across a retailer’s entire ecosystem. Imagine it connecting with design software, inventory systems, and marketing platforms to not only suggest actions but help execute them seamlessly. It’s about creating a unified workflow where AI drives smarter, faster decisions at every touchpoint, ultimately embedding itself as a core partner in how retailers operate day-to-day.
What is your forecast for the role of predictive AI tools like Ellis in the retail industry over the next decade?
I believe predictive AI tools like Ellis will become indispensable in retail over the next ten years. As consumer behavior continues to shift rapidly and competition intensifies, retailers will rely on these tools to anticipate trends, optimize operations, and personalize experiences at scale. We’re likely to see AI evolve from a supportive role to a central driver of strategy, where systems like Ellis not only predict outcomes but actively shape business models to stay ahead of the curve. It’s an exciting time for the industry.