Modus Data Production-grade AI Modus Data delivers pragmatic AI, machine learning and data engineering services from Therwil, Switzerland. Our founder brings over 10 years of enterprise AI experience for regulated industries, with a strong focus on pharma, medical data and Generative AI production workflows.
Natively Integrated with Your Enterprise Ecosystem
Advanced deep learning and large language models hold transformative potential—but only if they survive contact with real-world infrastructure. Modus Data bridges the gap between complex research notebooks and resilient, high-ROI enterprise software.
Many organizations can spin up a quick AI prototype. Very few can build models that run reliably at scale, handle nuanced compliance constraints, and actively save millions in operational costs.
Modus Data designs, builds, and deploys custom AI systems built for mission-critical operations. Whether you are automating highly sensitive document validation in life sciences, processing massive multi-terabyte computer vision foundation models, or predicting multi-layered fraud patterns in telecommunications, we deliver code that scales.
Transforming unstructured data into verified, compliant business logic. We design specialized generative AI pipelines that detect granular errors, automate complex compliance checks, and summarize high-stakes regulatory documentation.
Going beyond basic regressions. We deploy sophisticated architectures like Temporal Convolutional Networks (TCNs) and Gated Recurrent Units (GRUs) to solve complex time-series forecasting, fraud detection, and multi-modal classification problems.
Translating complex data landscapes into clear strategic value. We bridge the gap between low-level engineering and executive execution, transforming raw data matrices into optimized organizational roadmaps and measurable cost reductions.
An AI model is only as good as its deployment. We build the infrastructure, data pipelines, and validation frameworks required to monitor, update, and secure your models in production environments.
Targeted execution for defined deliverables such as LLM/RAG PoC development, architectural audits, and master data resolution pipelines.
Strategic leadership, MLOps governance, and code reviews on a recurring basis for scale-ups and enterprise teams needing senior guidance without a full-time hire.
Direct integration into existing delivery teams to lead high-stakes AI architecture or data engineering initiatives during critical project phases.
Principal-led execution backed by a vetted network of specialist data engineers and MLOps consultants for large-scale enterprise deployments.
Automotive Enterprise Generative AI: Architected and productized an end-to-end classification and automated correspondence summarization system, linking localized open-source LLMs hosted via container apps directly into corporate CRM ecosystems.
Pharma & MedTech Compliance: Developed an automated LLM validation pipeline capable of catching critical human errors missed during standard medical writing reviews within Clinical Study Reports (CSRs).
Foundational Deep Learning & Digital Pathology: Orchestrated training pipelines and high-performance deep learning architectures (including ViTs and ConvNeXT) trained on over 100+ TB of unstructured, complex digital pathology and medical imaging layers.
Enterprise Data Intelligence & Reporting: Engineered secure cloud data platforms and Kimball-standard warehouses utilizing Azure Databricks, PySpark, and Azure Data Factory—establishing a singular source of truth mapped directly to custom executive reporting suites in PowerBI.
Telecom Fraud Mitigation: Designed and deployed an enterprise-wide fraud prediction engine from scratch, securing infrastructure and preventing millions in annual revenue leakage.
Enterprise AI & Data Architecture Experience
Your journey toward production-grade AI starts with a technical conversation. A direct principal-led engagement keeps the design quality high while avoiding bloated consulting layers or junior staffing overhead.