.png)
Genomma Lab is one of Latin America’s leading pharmaceutical and personal care companies, developing and marketing more than 60 consumer health brands across 18 countries and reaching over 500,000 retail points of sale.
Managing commercial performance at that scale requires teams to work with information from many different sources. Genomma Lab relied on internal sales data, customer data, media performance, market research, and third-party point-of-sale information. Each source had a different structure, level of detail, and reporting cadence, making it difficult to build a consistent view of the business.
Genomma Lab partnered with Riley to explore a more scalable way to integrate these datasets and make commercial information easier for business teams to access. Through the partnership, the company reduced its reliance on purely manual data preparation, explored conversational access to business information, and gained new technical approaches that influenced its broader data and AI strategy.
Genomma Lab’s commercial teams worked with five major categories of data:
The problem was not a lack of information. It was that every dataset operated differently.
Some sources were updated weekly, while others arrived monthly. Each used different levels of granularity, identifiers, formats, and time periods. Before analysts could answer a commercial question, they first had to manually standardize and reconcile the underlying information.
This work was repeated whenever new data arrived.
The manual process slowed analysis and made it harder for business users to access information without help from technical teams. It also limited Genomma Lab’s ability to combine sales, market, customer, and media signals into a more complete commercial view.
Genomma Lab needed a stronger foundation for connecting data, improving forecasting workflows, and eventually allowing business teams to ask questions without first assembling information by hand.
The original goal of the partnership was to create an intelligent business assistant capable of answering questions using Genomma Lab’s commercial information.
Rather than requiring users to navigate several systems or wait for a custom report, the proposed experience would allow business teams to ask questions conversationally and retrieve relevant information from connected datasets.
This required more than adding a chatbot on top of isolated files. The underlying information first had to be standardized, governed, and connected in a way that accounted for differences in granularity, timing, and structure.
Riley provided a technical foundation for bringing these sources together and demonstrated how a conversational interface could sit on top of a unified commercial data layer.
Genomma Lab began working with Riley following a recommendation.
As the partnership developed, Riley’s speed and technical capabilities became the most valuable parts of the engagement.
Riley demonstrated an approach that combined data integration, modern retrieval methods, data-lake principles, and a conversational business interface.
For Genomma Lab’s data science team, the value extended beyond the immediate platform. The engagement gave the team a practical example of how vector-based architectures and connected enterprise data could support a new generation of internal business tools.
Riley worked with Genomma Lab to bring together data with different structures, update schedules, and levels of detail.
The objective was to create a centralized foundation capable of supporting questions across multiple commercial domains rather than treating each dataset as a separate analytical project.
This included exploring how information from sales, customers, markets, media, and points of sale could be integrated into a common architecture and made available through a business-facing interface.
The work helped Genomma Lab evaluate a more scalable alternative to repeatedly consolidating the same information by hand.
It also showed how a unified intelligence layer could help business and technical teams work from the same underlying commercial context.
One of the clearest outcomes of the engagement was its influence on Genomma Lab’s internal technology strategy.
The company already had a mature data organization and an established philosophy around governance, information curation, modeling, and business-facing analytics. Riley did not replace that foundation. Instead, the partnership introduced additional techniques and showed how those capabilities could be combined within a modern AI architecture.
Genomma Lab’s team studied Riley’s approach to data lakes, vector databases, and information integration. Those ideas helped inform the company’s own internal development efforts.
The collaboration became a two-way exchange. Genomma Lab shared forecasting methods and commercial expertise, while Riley demonstrated new technical integration approaches.
This knowledge sharing helped both teams strengthen their capabilities.
The partnership addressed two related layers of commercial intelligence.
The first was the underlying data foundation: governance, curation, integration, and the ability to connect datasets that operated at different levels of granularity and timing.
The second was the business-facing experience: giving users a conversational way to ask questions and retrieve information without needing to understand the technical architecture behind it.
Riley demonstrated how those two layers could work together.
Although the business-facing product did not reach the full final state Genomma Lab originally envisioned during the engagement, the work provided a valuable technical model for how the company could continue developing its own data and AI capabilities.
It also reinforced an important principle: conversational AI is only as valuable as the quality, governance, and connectivity of the business data beneath it.
The Riley partnership helped Genomma Lab move toward a more scalable and business-oriented approach to commercial intelligence.
The engagement contributed to:
The project demonstrated that meaningful enterprise AI begins with connected, well-governed business information—not simply a conversational interface.
David emphasized that the experience of working with the Riley team was open, accessible, and collaborative.
Rather than imposing a rigid implementation model, both teams shared methods and learned from one another. Genomma Lab contributed its expertise in forecasting and commercial analytics, while Riley shared modern approaches to integrating and retrieving information.
That exchange became one of the most valuable elements of the partnership.
For Genomma Lab, the Riley engagement was more than a data-integration project. It provided a practical model for connecting commercial information, exposing it through AI, and advancing the company’s broader vision for enterprise intelligence.