BharAI-Lab is a research-focused AI lab working on foundation models, multimodal perception, and evaluation-first AI systems.

We build open, reproducible, and production-aware AI – from self-supervised vision to RAG & agents and cloud-native ML pipelines.

🇩🇪 Based in Germany • 🧠 Applied & Responsible AI • 🧪 Research → Systems → Real-World Impact
📧 Contact: suraj.unisiegen@gmail.com • 🌐 GitHub: BharAI-Lab

• Focus: Foundation models, multimodal learning, RAG, agents, evaluation & MLOps
• Style: Research-grade ideas with industry-grade engineering
• Domains: Legal & compliance, mobility & driver monitoring, analytics for digital products
• Values: Reproducibility · Evaluation-first · Privacy & GDPR · Real-world constraints

• Retrieval-Augmented Generation over specialised corpora (legal, regulatory, domain-specific)
• Tool-using agents and structured outputs (JSON, SQL, code) for automation and analytics
• Evaluation pipelines: hallucination, grounding, robustness, bias and failure analysis

• Vision Transformers and DINO-style self-supervised pretraining
• Driver monitoring & distraction detection under imbalanced, noisy, real-world conditions
• Fusion of vision, language, and temporal signals; deployment on constrained hardware

• Benchmarks and synthetic test suites for RAG, agents, and vision systems
• Out-of-distribution behaviour, calibration, safety margins, and interpretability
• GDPR-aware data workflows and transparent documentation of limitations & risks

• Cloud-native ML on Azure and GCP (containers, GPUs, serverless workflows)
• CI/CD for ML: training → evaluation → packaging → deployment → monitoring
• Experiment tracking, lineage, and reproducible pipelines

We treat research as a software discipline:

1. Reproducible by Default

   • Clear setup instructions, pinned environments, and scripted workflows
   • Config-driven experiments and explicit seeds where it matters

2. Evaluation-First

   • Metrics for performance, robustness, latency, and sometimes fairness
   • Ablations and diagnostics considered part of the deliverable, not extras

3. Production-Aware Research

   • Prototypes assume a path to deployment: logging, monitoring, fail-safes
   • Containerised services and infra-as-code where relevant

4. Ethics, Privacy & Compliance

   • Strong focus on data minimisation and GDPR alignment
   • Transparent notes on data sources, intended use, and limitations

When a repository corresponds to a paper, thesis, or technical report, it will include:

• CITATION.cff and/or BibTeX
• Links to arXiv / conference / workshop pages
• Notes on dataset availability, licenses, and reproduction details

If you use our work in your research or products, please cite the corresponding artifacts.

We are open to collaboration with:

• Research groups in foundation models, multimodal, evaluation & safety
• Industry partners in mobility, legal/compliance, finance, and digital products
• Open-source contributors who like research-grade engineering

Get in touch:

• Open an issue or discussion in a relevant repository
• Or email: suraj.unisiegen@gmail.com

BharAI-Lab – building rigorous, future-ready AI systems for the next generation of research and real-world impact.