Ever since the introduction of ChatGPT in November 2022, Generative AI has reshaped the AI industry. Companies like Amazon, Google, Microsoft, and of course SAP have accelerated innovation, recognizing the enormous value Generative AI and Large Language Models (LLMs) bring to enterprise operations.
General‑purpose LLMs excel at understanding language, reasoning over text, and identifying patterns in unstructured information. They are creative, adaptive, and capable of leveraging data from files, documents, and multimedia across diverse data systems.
The Motive
However, when it comes to actually predicting business outcomes, LLMs fall short. Why?
Because they are not designed for high‑precision, multi‑step reasoning over large, enterprise‑grade tabular datasets.
And this is where the majority of business data resides: structured tables, such as GL accounts, invoices, inventory, sales records, financial transactions, and countless others. Not in free‑form text.
Everyone wants to be able to predict business outcomes like:
Relational: optimized for structured relational business dataPre‑trained: powered by tens of thousands of GPU hours (no more endless classical ML training cycles)Transformer‑based: performs logical, not linguistic, transformations (e.g., filtering, joining, aggregating, and multi‑step reasoning)
Instead of hundreds of ML models, you now can use one single foundation model for many predictive tasks.
One of the most disruptive features of RPT‑1 is its in‑context learning capability. Instead of training or fine‑tuning multiple modes, you simple provide historical rows of data and request predictions for new rows. Prediction cases like:
Customer churn predictionLate delivery predictionLate payment predictionSales conversion predictionAnd many more
RPT‑1 Performance Benchmark
RPT-1 outperforms both LLM and classical ML for tabular data and high value agentic cases. Here are some stats so far:
50x faster than LLMsUp to 2x Prediction Quality vs Narrow AI/ML ModelsUp to 3.5x Prediction Quality vs LLMs100,000x fewer GPU FLOPs50,000x Less Energy consumption vs LLMs*
*comparable tasks on a NVIDIA H100, as a benchmark. Sources (TechEd, Blog) .
Why are these stats so important? On top of the benefits you can already imagine like time and resource savings; according to Gartner 40% of agentic-AI projects are likely to be canceled by end of 2027, mainly because of high cost, complexity, or unclear business value (source). RPT‑1 directly addresses these challenges.
Sales Conversion Prediction Example Use Case
During SAP TechEd, a compelling demo showcased how RPT‑1 integrates with SAP’s Agentic AI.
Imagine a sales team wanting to prioritize leads based on their likelihood to convert. Instead of manually training an ML model, a data analyst can use Joule to generate SQL code leveraging RPT‑1’s PREDICT function.
Steps include:
Join sales inquiries, historical performance, customer attributes, and other relevant data into a Data Product in SAP Business Data Cloud.Expose the Data Product to SAP HANA Cloud via zero‑copy.Use RPT‑1 to instantly generate a new prediction column “sales_conversion_probability”; without training or tuning.
The model derives patterns directly from the historical data and produces high‑quality prediction scores.
You can watch the full demo here (jump to minute 35:23).
Available RPT‑1 Versions
SAP RPT‑1 has been generally available since Q4 2025. You can choose from:
Try it yourself!
Don’t have access to a BTP account yet? here is an entry point:
Sign up for a 30-day SAP Generative AI Hub trialFollow this step-by-step guide to get started by @sherene_tan
Conclusion
SAP RPT‑1 represents a major shift in how enterprises will deliver predictive insights in the GenAI era. While LLMs excel at language understanding, they are not built for structured, relational business data. RPT‑1 closes this gap and eliminates the complexity of traditional machine learning, drastically reducing cost and compute.
With SAP RPT-1’s foundation model purpose‑built for enterprise-grade tabular data, the objective is to enable organizations to operationalize predictive insights faster, more efficiently, and more accurately than ever before.
Ever since the introduction of ChatGPT in November 2022, Generative AI has reshaped the AI industry. Companies like Amazon, Google, Microsoft, and of course SAP have accelerated innovation, recognizing the enormous value Generative AI and Large Language Models (LLMs) bring to enterprise operations. General‑purpose LLMs excel at understanding language, reasoning over text, and identifying patterns in unstructured information. They are creative, adaptive, and capable of leveraging data from files, documents, and multimedia across diverse data systems.The MotiveHowever, when it comes to actually predicting business outcomes, LLMs fall short. Why?Because they are not designed for high‑precision, multi‑step reasoning over large, enterprise‑grade tabular datasets.And this is where the majority of business data resides: structured tables, such as GL accounts, invoices, inventory, sales records, financial transactions, and countless others. Not in free‑form text.Everyone wants to be able to predict business outcomes like:What is the probability of converting a sale?Which customers are likely to pay late?Who is at risk of churn?…Historically, answering these questions required traditional machine learning like classification models, linear regression, etc. (a.k.a Traditional AI or Narrow AI). The problem with this is that classical ML requires to train a model per task, which easily can lead to hundreds of separate models hard to maintain in the long run, making it very cumbersome, expensive and extremely difficult to scale. The Solution: SAP RPT-1 To solve this, SAP introduced in TechEd 2025 the first foundation model specifically designed for structured enterprise data: Relational Pre‑Trained Transformer 1 (RPT‑1). The model is trained natively on tabular business datasets and is engineered to understand rows, columns, joins and business semantics out of the box. As the name suggests, RPT‑1 is:Relational: optimized for structured relational business dataPre‑trained: powered by tens of thousands of GPU hours (no more endless classical ML training cycles)Transformer‑based: performs logical, not linguistic, transformations (e.g., filtering, joining, aggregating, and multi‑step reasoning)Instead of hundreds of ML models, you now can use one single foundation model for many predictive tasks.One of the most disruptive features of RPT‑1 is its in‑context learning capability. Instead of training or fine‑tuning multiple modes, you simple provide historical rows of data and request predictions for new rows. Prediction cases like:Customer churn predictionLate delivery predictionLate payment predictionSales conversion predictionAnd many moreRPT‑1 Performance BenchmarkRPT-1 outperforms both LLM and classical ML for tabular data and high value agentic cases. Here are some stats so far:50x faster than LLMsUp to 2x Prediction Quality vs Narrow AI/ML ModelsUp to 3.5x Prediction Quality vs LLMs100,000x fewer GPU FLOPs50,000x Less Energy consumption vs LLMs**comparable tasks on a NVIDIA H100, as a benchmark. Sources (TechEd, Blog) .Why are these stats so important? On top of the benefits you can already imagine like time and resource savings; according to Gartner 40% of agentic-AI projects are likely to be canceled by end of 2027, mainly because of high cost, complexity, or unclear business value (source). RPT‑1 directly addresses these challenges. Sales Conversion Prediction Example Use CaseDuring SAP TechEd, a compelling demo showcased how RPT‑1 integrates with SAP’s Agentic AI.Imagine a sales team wanting to prioritize leads based on their likelihood to convert. Instead of manually training an ML model, a data analyst can use Joule to generate SQL code leveraging RPT‑1’s PREDICT function.Steps include:Join sales inquiries, historical performance, customer attributes, and other relevant data into a Data Product in SAP Business Data Cloud.Expose the Data Product to SAP HANA Cloud via zero‑copy.Use RPT‑1 to instantly generate a new prediction column “sales_conversion_probability”; without training or tuning.The model derives patterns directly from the historical data and produces high‑quality prediction scores.You can watch the full demo here (jump to minute 35:23). Available RPT‑1 VersionsSAP RPT‑1 has been generally available since Q4 2025. You can choose from:SAP RPT‑1 Small: optimized for speed and efficiencySAP RPT‑1 Large: optimized for highest accuracy by using more capacitySAP RPT‑1 OSS (Open Source): available on HuggingFace and Github for exploration and learningTry it yourself!Don’t have access to a BTP account yet? here is an entry point:Sign up for a 30-day SAP Generative AI Hub trialFollow this step-by-step guide to get started by @sherene_tan ConclusionSAP RPT‑1 represents a major shift in how enterprises will deliver predictive insights in the GenAI era. While LLMs excel at language understanding, they are not built for structured, relational business data. RPT‑1 closes this gap and eliminates the complexity of traditional machine learning, drastically reducing cost and compute.With SAP RPT-1’s foundation model purpose‑built for enterprise-grade tabular data, the objective is to enable organizations to operationalize predictive insights faster, more efficiently, and more accurately than ever before. 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