Forget Fine-Tuning: SAP’s RPT-1 Brings Ready-to-Use AI for Business Tasks
SAP aims to displace more general large language models with the release of its own foundational “tabular” model, which the company claims will reduce training requirements for enterprises. The model, called SAP RPT-1, is a pre-trained model with business and enterprise knowledge out of the box. SAP calls it a Relational Foundation Model, meaning it can do predictions based on relational databases even without fine-tuning or additional training.Walter Sun, SAP’s global head of AI, told VentureBeat in an interview that the value of the new model lies in its ability to perform various enterprise tasks, such as predictive analytics, out of the box. “Everyone knows about language models, and there’s a bunch of good ones that already exist,” Sun said. “But we trained the model on data on business transactions, basically Excel spreadsheets, and so we have a model that can do predictive analytics where the value is that it’s out of the box, meaning you don’t need to have specifics of a company to do tasks analogous to a language model.” Sun said that right out of the gate, RPT-1 can essentially build out a business model for enterprises based on its knowledge gained from data from SAP’s decades of information. Organizations can plug the model directly into applications, even without additional fine-tuning.RPT-1, SAP’s first large family of AI models, will be generally available in “Q4 of 2025” and be deployed via SAP’s AI Foundation. While RPT-1 is currently available, the company stated that additional models will be made available soon, including an open-source, state-of-the-art model. SAP will also release a no-code playground environment to experiment with the model.
Tabular models vs LLMs
Tabular or relational AI models learned from spreadsheets, unlike LLMs, which learned from text and code. RPT-1 not only understands numbers and the relationships between different cells, but it’s also able to provide more structured and precise answers. When enterprises decide to use RPT-1, they can add more direction to the model through a bit of context engineering, since the model is semantically aware and learns based on how it is being used. SAP researchers first proposed the idea that tabular models can both exhibit semantic awareness and learn from content through a paper published in June. It proposed ConTextTab introduced context-aware pretraining. It utilizes semantic signals, such as table headers or column types, to guide model training, enabling the model to build a relational structure with the data. It’s this architecture that makes the model work best for tasks with precise answers, such as for financial or enterprise use cases.The RPT models build on the ConTextTab work that lets it learn structured business data, say from SAP’s knowledge graph, and then be able to add more context through usage. SAP researchers did test ConTextTab against benchmarks, saying it “is competitive” against similar models like TabPFN and TabIFL. Industry-specific models continue to grow
Many enterprises prefer to fine-tune general LLMs like GPT-5 or Claude, to basically retrain the model to answer only questions relevant to their business. However, a shift towards industry-specific models has begun to take root. Sun said that his experience at a previous company, building a very narrow, highly customized AI model for sentiment analysis, influenced a lot of what makes RPT-1 different. “It was a very customized model, a narrow model that takes specific feedback for specific products but it wasn’t scalable,” Sun said. “When LLMs came about, that one model measures sentiment. But there are use cases that we can do that LLMs cannot do.”He said these use cases include predictions, such as determining when a shopper will return to a grocery store, which may involve numerical analysis along with an understanding of the shopper’s buying habits. However, some LLMs have begun integrating into spreadsheets, and AI model providers encourage users to upload similar data to teach them context. Microsoft added new capabilities to Copilot, including the ability to work in Excel. Anthropic integrated its Claude model with Excel, complementing its Claude for Finance service. Chinese startup Manus also offers a data visualization tool that understands spreadsheets, and ChatGPT can create charts from uploaded spreadsheets and other data sources. However, SAP noted that it is more than just reading a spreadsheet; RPT-1 should stand out amongst its competitors because it requires fewer additional pieces of information about a business to provide its responses.
Tabular models vs LLMs
Tabular or relational AI models learned from spreadsheets, unlike LLMs, which learned from text and code. RPT-1 not only understands numbers and the relationships between different cells, but it’s also able to provide more structured and precise answers. When enterprises decide to use RPT-1, they can add more direction to the model through a bit of context engineering, since the model is semantically aware and learns based on how it is being used. SAP researchers first proposed the idea that tabular models can both exhibit semantic awareness and learn from content through a paper published in June. It proposed ConTextTab introduced context-aware pretraining. It utilizes semantic signals, such as table headers or column types, to guide model training, enabling the model to build a relational structure with the data. It’s this architecture that makes the model work best for tasks with precise answers, such as for financial or enterprise use cases.The RPT models build on the ConTextTab work that lets it learn structured business data, say from SAP’s knowledge graph, and then be able to add more context through usage. SAP researchers did test ConTextTab against benchmarks, saying it “is competitive” against similar models like TabPFN and TabIFL. Industry-specific models continue to grow
Many enterprises prefer to fine-tune general LLMs like GPT-5 or Claude, to basically retrain the model to answer only questions relevant to their business. However, a shift towards industry-specific models has begun to take root. Sun said that his experience at a previous company, building a very narrow, highly customized AI model for sentiment analysis, influenced a lot of what makes RPT-1 different. “It was a very customized model, a narrow model that takes specific feedback for specific products but it wasn’t scalable,” Sun said. “When LLMs came about, that one model measures sentiment. But there are use cases that we can do that LLMs cannot do.”He said these use cases include predictions, such as determining when a shopper will return to a grocery store, which may involve numerical analysis along with an understanding of the shopper’s buying habits. However, some LLMs have begun integrating into spreadsheets, and AI model providers encourage users to upload similar data to teach them context. Microsoft added new capabilities to Copilot, including the ability to work in Excel. Anthropic integrated its Claude model with Excel, complementing its Claude for Finance service. Chinese startup Manus also offers a data visualization tool that understands spreadsheets, and ChatGPT can create charts from uploaded spreadsheets and other data sources. However, SAP noted that it is more than just reading a spreadsheet; RPT-1 should stand out amongst its competitors because it requires fewer additional pieces of information about a business to provide its responses.
