Executive Summary

We partnered with MODO to prototype an AI-powered promotion discovery experience and help their leadership team envision how generative AI could transform the way users find offers inside the app.

At the time, MODO was exploring how emerging AI technologies could improve the visibility and usability of its growing catalog of promotions. Through the development of PromoBot, an experimental AI-powered assistant, Muttdata helped demonstrate how natural language understanding could power a new generation of promotion discovery tools.

The prototype validated a powerful idea: users could search for deals using everyday language rather than rigid keywords.

This early MVP played a key role in shaping the internal product vision that later evolved into SearchAI, Modo’s AI-powered promotion search engine.

About the company

MODO is a rapidly growing digital payments platform that connects banks, users, and retailers within a unified ecosystem. Focused on making it easier to pay, send, and receive money, MODO has become a core financial tool for millions of users across Argentina. As their commercial team expanded its network of promotional partnerships, one persistent challenge emerged: users struggled to find the offers that mattered most to them.

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The Challenge

A Problem of Visibility, Relevance, and Engagement

Making Promotions Easier to Discover

Modo offers hundreds of promotions across banks, retailers, and product categories. However, the existing search experience inside the app was designed around traditional keyword-based logic.

This meant that users often needed to know exactly what to search for in order to find relevant offers. Slight variations in spelling or phrasing could lead to empty results, making discovery difficult.

For example, a user typing “supermark” instead of “supermarket” could receive no results at all.

As the number of promotions continued to grow, MODO’s team began exploring how artificial intelligence could improve the experience and make promotions easier to discover.

The opportunity was not only to improve search, but to explore how generative AI could act as a recommendation assistant inside the app.

MODO wanted to test whether AI could:

• Interpret natural language queries instead of exact keywords

• Help users discover promotions they might not otherwise find

• Increase engagement with related promotional suggestions and AI-generated comments.

• Demonstrate internally how GenAI could power future product capabilities

To explore this vision, MODO partnered with Muttdata to develop an MVP.

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Our solution

Prototyping AI-Powered Promotion Discovery with PromoBot

To explore how generative AI could improve promotion discovery, Muttdata and MODO collaborated on the development of PromoBot, a chatbot prototype powered by Large Language Models.

PromoBot allowed users to search for promotions using natural language instead of rigid keywords. Users could ask questions such as:

“I’m going out with friends on Friday — are there any bar promos? or

“Where can I get discounts with my bank card this week?”

Instead of requiring precise queries, the system interpreted the intent behind the request and returned relevant promotions.

This prototype allowed MODO’s product and leadership teams to visualize how AI could fundamentally improve the promotion discovery experience.

This proof of concept marked MODO’s first step into generative AI. PromoBot included several experimental capabilities designed to test how AI could enhance the promotion search experience:

Natural Language Search

Users could search promotions using conversational queries instead of exact keywords.

Contextual Recommendations

The assistant interpreted the user’s intent and suggested relevant promotions based on categories, merchants, and dates.

Promotion Comparison

PromoBot could compare two promotions and explain which one offered better value.

Moderation Layer

A safety layer ensured that responses remained appropriate and relevant.

MVP Search Use Cases

During the MVP phase, Muttdata and MODO focused on several key scenarios where traditional search struggled:

Search by Bank

Users could ask for promotions associated with a specific bank.

Search by Category

The assistant detected categories such as restaurants, fuel, or clothing.

Search by Date

Queries like “tomorrow”, “this weekend”, or “next Friday” were interpreted and matched to valid promotions.

Search by Merchant

Mentions of specific stores or brands triggered relevant promotion results.

Search by Product Type (SKU)

Searches like “jeans” or “ice cream” were mapped to relevant product categories.

Search by Events or Holidays

Queries like “Christmas” or “Mother’s Day” returned seasonal promotions.

Architecture

Under the hood, PromoBot leveraged a cloud-native architecture powered by:

• Large Language Models via Amazon Bedrock

• REST APIs built with FastAPI

• Containerized deployment using Docker and AWS ECS

• Observability through OpenTelemetry and Datadog

The system interpreted user queries, extracted structured filters (such as category, date, or merchant), and used them to retrieve and rank promotions from MODO’s existing promotion services.

From PromoBot to SearchAI

The insights gained from the PromoBot MVP helped Modo refine the product vision for AI-powered promotion discovery.

As the concept matured, the team decided that AI should not live inside a chatbot interface, but instead power the core promotion search experience directly inside the app.

After our collaboration, MODO continued evolving SearchAI and enhanced the search engine by incorporating semantic search. This optimization reduced LLM calls, improving latency, cost efficiency, and overall scalability, ultimately enabling the product to scale in production.

The result of this evolution is SearchAI, a new generation promotion search engine now entering production.

While the final product differs significantly from the initial prototype, the early MVP played an important role in demonstrating the potential of AI-driven promotion discovery and helping align leadership around the vision.

The results

While PromoBot was developed as a prototype, the ideas tested during the MVP phase helped inform the design of the production SearchAI engine.

These results validated the core hypothesis behind the project: that natural language search and AI-powered recommendations can significantly improve promotion discovery.

Ready to unlock the full potential of your company? Do a discovery call with us to discover how our expertise can elevate your capabilities.

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