SAP Business AI Research | Open-access Publications and Blogs

Featured research

ConTextTab: A Semantics-Aware Tabular In-Context Learner

By leveraging LLM embeddings, ConTextTab successfully integrates semantics from table features into tabular prediction, shining on data with high semantic content such as free text or descriptive categories.

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RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphs

We introduce RELATE (Relational Encoder for Latent Aggregation of Typed Entities), a schema-agnostic, plug-and-play feature encoder that can be used with any general purpose GNN.

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Foundation Models for Tabular Data within Systemic Contexts Need Grounding

We propose Foundation Models for Semantically Linked Tables (FMSLT) to advance the understanding of structured enterprise data. Enterprise tables are interconnected through operational logic and semantic relationships that define how businesses operate. Recognising and modelling these connections is essential for capturing the true nature of enterprise data.

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Open-Source Enterprise Datasets

We introduce SALT and SALT-KG, the first enterprise datasets built from real customer ERP systems. They combine rich, linked business tables with a curated knowledge graph capturing semantic context. Together, they lay the foundation for advancing foundation models that truly understand structured enterprise data.

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NEWS

Paper and Open Source Foundation Model on Tabular Data: SAP-RPT-1-OSS

We have published our ConTextTab research paper at NeurIPS 2025 (spotlight paper) and provided an open weight version of our model as SAP-RPT-1-OSS.

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Who we are

At SAP Business AI Research, we serve as the bridge between academia and industry, dedicated to advancing next-generation AI systems. Our research addresses the complexities of real-world enterprise environments by integrating cutting-edge AI techniques with domain-specific challenges. We focus on two main research tracks to ensure that our models are not only powerful but also practical, trustworthy, and scalable.

Research areas

Track A: Structure - Aware Foundation Models

We develop foundation models that reason over complex, linked business data—spanning tables, time series, and graphs. By integrating structural awareness, multimodal inputs, and causal reasoning, our models enable advanced Business AI for analysis, forecasting, and decision-making.

Table representation learning

Learning tabular data representations via table-native and language-based models, integrating business data for advanced reasoning.

Graph neural networks

Using Graph Neural Networks to model relational tabular data, enabling accurate predictions and deeper insights in enterprise AI.

Business knowledge graph

Building enterprise knowledge graphs to enable precise, context-aware queries across diverse business data.

Agentic AI

Building self-improving agents for reliable, goal-driven automation in enterprise systems.

Coding LLM (ABAP)

Empowering enterprise software development with domain-specific ABAP foundation models for intelligent coding assistance.

Track B: Trustworthy AI

Our research develops AI systems that are robust, fair, transparent, and aligned with human values—essential for real-world enterprise use. We focus on robustness, explainability, fairness, privacy, and alignment with domain-specific constraints to ensure reliable and responsible AI deployment.

Differential privacy

We develop efficient deep learning models that save resources and protect privacy.

Data confidentiality

We ensure data confidentiality by protecting structured data and validating privacy through audits and attacks.

Model protection

Analysing sentiments in text using neural embedding and attention.

Security testing

Enhancing model transparency by making predictions explainable.

Human-Alignment

Extracting data from documents using NLP and computer vision.

Careers

Join us and build the future of Business AI

Work with rich datasets to find machine learning-based solutions to real-world problems in close collaboration with our global network of research partners.