Project Overview
AB-InBev, one of the biggest names in the food and beverage industry, partnered with Tart Labs to introduce AI into their recommendation workflows. The goal? To make smarter, faster, and more relevant product recommendations across their platforms. Tart Labs stepped in to build a fully customized AI-based recommendation engine that could help AB-InBev drive user engagement and improve decision-making with intelligent suggestions, backed by real-time data.
About AB-InBev
Anheuser-Busch InBev (AB-InBev) is a global brewing giant with a presence in over 150 countries and a portfolio that includes some of the world's most iconic beverage brands. With such a vast product range and diverse customer base, staying ahead of market trends and understanding consumer behavior at scale isn't easy. That's where AI comes in. AB-InBev wanted to explore smarter ways to connect customers with the right products, without guesswork.
The Challenge
Before this project kicked off, AB-InBev faced a familiar yet complex problem: product overload. With hundreds of options available, it was difficult to deliver the right recommendation to the right user at the right time. Manual processes and basic filters weren't cutting it anymore. The company needed a dynamic solution that could adapt to individual user preferences, regional trends, and past purchase behavior, without adding friction to the user experience. That's when Tart Labs was brought in to rethink the recommendation approach with AI at the core.
Our Solution
Features That Made a
Features That Made a
We didn't just build a recommendation system, we designed a full-featured AI engine that could grow with AB-InBev's needs. Every functionality was built with a purpose, and every module worked together to deliver real value. Take a look at the features that made this AI recommendation engine smart, adaptable, and built with the user in mind:
Personalized Recommendations
Every user saw product suggestions based on their own activity, recent views, purchases, preferences, and even what similar users liked. This added a personal touch to every interaction and boosted engagement right from the first click.
AI-Driven Smart Search
Users could type in a product name, a flavor, or even a vague idea, and the system responded with spot-on results. The search module learned over time, correcting typos, predicting intent, and offering relevant suggestions even before users finished typing.
Real-Time Behavior Tracking
From clicks to scrolls to time spent on product pages, the engine tracked everything in real time. This allowed it to constantly adjust recommendations, keeping them relevant and timely based on user intent.
Context-Aware Suggestions
The system took into account factors like location, time of day, and seasonality. For instance, a summer promotion in Brazil triggered a different recommendation set than a winter campaign in Europe.
Self-Learning Algorithms
Every interaction made the system smarter. Using machine learning, the engine continuously adapted based on what users ignored, clicked, or purchased, without any manual intervention.
Admin Dashboard & Control Panel
We built a sleek, intuitive dashboard where AB-InBev teams could monitor performance, view top-performing products, adjust filters, and run A/B tests on recommendation models, all from one place.
Omnichannel Integration
Whether customers accessed the platform from a mobile app, web portal, or internal sales tool, the recommendations stayed consistent. The system was designed to work across devices and channels without missing a beat.
Multi-Language Support
To support AB-InBev's global footprint, the platform supported multiple languages and regional content so every user got localized experiences that felt native.
Inventory-Aware Suggestions
We linked the system with AB-InBev's inventory APIs to avoid recommending out-of-stock items. That meant fewer dead ends and smoother conversions.
Flexible APIs
The backend was built with modular, RESTful APIs, making it easy to extend or customize for future use cases like chatbots, voice assistants, or third-party integrations.
Book a free consultation
Ready to Build Smarter AI Solutions?
Just like AB-InBev, your business can tap into AI to drive smarter recommendations, boost product discovery, and improve conversions. If you're in retail, FMCG, or beyond - we're here to power your next big move with intelligent, scalable tech.
Write us:sales@tartlabs.com
Our Solution
We built a smart, AI-powered recommendation engine that could do more than just "suggest." It learned. It adapted. And it delivered. At Tart Labs, we designed the system to analyze customer behavior, preferences, product interactions, and regional trends, all in real time. If it was helping sales teams suggest the right beverages to retailers or supporting marketing with targeted campaigns, our solution acted as a brain behind the scenes. The engine used a mix of collaborative filtering, content-based recommendations, and machine learning algorithms to provide personalized, data-backed suggestions that actually made sense, no guesswork, no fluff. This wasn't a one-size-fits-all model. It was scalable, flexible, and trained on AB-InBev's real-world data. So, if the user was a distributor, a retailer, or an end consumer, they'd see smarter, more relevant recommendations every time they logged in. The outcome? Higher conversions, quicker choices, and a smoother experience for every user interaction
Tech Stacks
Technology Stack
Technology Stack
The tools, frameworks, and AI models used in developing AB-InBev's recommendation system helped boost user satisfaction, increase product visibility, and improve conversion rates, all powered by one robust AI engine built by Tart Labs.
Results & Impact
The AI recommendation system we delivered for AB-InBev didn't just meet expectations, it changed the game. Right after implementation, the company saw noticeable improvements in how customers discovered and engaged with their products. Users started discovering more relevant products, which kept them engaged longer on the platform. This improved browsing behavior directly contributed to better conversion rates and a noticeable rise in average order value across various locations.
Real-time Market Response
Thanks to real-time data processing and context-aware suggestions, AB-InBev could respond faster to market trends and customer preferences. The teams gained valuable insights through the analytics dashboard, helping them fine-tune marketing strategies and inventory planning.
Enhanced Customer Satisfaction
Overall, the system helped AB-InBev enhance customer satisfaction by delivering smarter, more relevant product experiences, strengthening brand loyalty, and driving measurable business growth.
Measurable Business Growth
In numbers, the company reported a noticeable uptick in sales within the first few months, alongside improved operational efficiency in product recommendations and campaign targeting. This project is a prime example of how combining AI with solid engineering can unlock new opportunities, even for a global enterprise.
Check out some other
Check out some other
Explore our success stories of providing software development solutions for different verticals.