Business AI Research
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.
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.
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. Recognizing and modeling these connections is essential for capturing the true nature of enterprise data.
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.
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.
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
Analyzing 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.
PhD Internship Program
As a PhD Intern you will work with a team of experienced researchers and applied scientists taking on challenges informed by scaling AI methods across and beyond the broad portfolio of SAP’s business software. You will have the chance to work with some of the richest data sets available in the world addressing problems that have impact on our customers.
Publications
SPRINT: Scalable Secure & Differentially Private Inference for Transformers
Francesco Capano, Jonas Böhler, Benjamin Weggenmann, PETS, 2026
Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis
Penny Chong, Harshavardhan Abichandani, Jiyuan SHEN, Atin Ghosh, Min Pyae Moe, Yifan Mai, Daniel Dahlmeier, ICLR, 2026
SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy
Francesco Capano, Jonas Böhler, Benjamin Weggenmann, IEEE SatML, 2026
Weishi Wang, Hengchang Hu, and Daniel Dahlmeier, EACL, 2026
Jiyuan Shen, Peiyue Yuan, Atin Ghosh, Yifan Mai, Daniel Dahlmeier, Industry Track, EACL, 2026
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky
Ashutosh Hathidara, Julien Yu, Sebastian Schreiber, ACL Findings, 2026
Unified Evaluation of Table Embedding Methods Across Multiple Benchmark Scenarios
Ali Younes, Saeed Ghoorchian, Maximilian Schambach, Johannes Höhne, DATA-FM workshop, ICLR, 2026
Relational In-Context Learning on Structured Data via Neighborhood Aggregation and Structural Information
Joseph Meyer, Afreen Shaikh, Mohammadi Reza, Dinesh Katupputhur Ramprasath, Karan Paresh, Roshan Reddy Upendra, Tom Palczewski, Mark Li, Summer Symposium, AAAI, 2026