Although this approach solved initial problems, over time it created a fragmented ecosystem that was costly to maintain. Its main limitation is redundancy. In other words, multiple isolated models are created, consuming resources to independently learn the same user behavior or entity without sharing their findings. In addition, due to latency and training cost constraints, these systems often suffer from a kind of structural amnesia, ignoring long-term history and focusing only on the most recent window of interactions.
The arrival of Large Language Models (LLMs) demonstrated the potential to change this dynamic through Foundation Models. Instead of training dozens of specific systems from scratch, a single large-scale model is trained in a semi-supervised manner on a general sequence prediction task. This allows the model to learn a deep, unified representation of the business’s core data, serving as a common platform that provides embeddings (vectors that mathematically represent interactions) or can be adapted through fine-tuning to power multiple downstream applications.
A paradigmatic example is the customer service chatbot. Companies can build a multimodal customer service platform capable of understanding text, audio, and images. In addition to responding to poorly formulated queries, it can generate tailored responses for each one, interact directly with the company’s other systems, and compile all this information into a summary of customer service statistics.
Another example can be found in financial applications. A traditional bank typically uses separate models to recommend loans, detect transactional fraud, and personalize the experience within its app. By moving toward a customer foundation model, the institution can centralize transactions, branch visits, mobile browsing, and phone interactions into a single chronological sequence. In this way, the model learns a rich and coherent representation of the customer’s financial lifecycle that directly supports all business units. This dramatically optimizes data usage and creates a more unified customer experience.
Similarly, an LLM-based foundation model can improve a company’s logistics by managing and forecasting inventory and calculating delivery times based on routes that it optimizes dynamically. It can also consolidate all the information a healthcare institution has about its patients, including physicians’ notes, into a standardized database that incorporates test results and other procedures. From this information, it can generate detailed reports for patients in non-technical language, freeing physicians to focus on other tasks.
The main strength of foundation models, however, lies in their flexibility. They can be implemented in virtually any process that previously involved exchanging data between separate—and not always standardized—applications and services. These can be replaced by a unified system that not only learns what each tool does but is also capable of combining data in innovative and, at the same time, more efficient ways.
Deploying these shared systems into production brings its own challenges. When the foundation model is updated, retraining can act as a kind of system “reset,” silently breaking existing connections with other applications. For this reason, closely monitoring the platform’s performance is recommended.
Centralizing capabilities within a foundation model enables companies to eliminate operational redundancies, mitigate fragmentation, and unlock the full potential of their historical data.