Industrial Data and AI

On a regulated site, confidentiality policy often rules out sending technical documentation or failure history through an external cloud service. That was exactly the situation for a pharma site where we built a fully on-premise RAG chatbot, with the model and the vector database hosted locally, because a cloud LLM was off the table from the start. That constraint is part of the brief from day one, not a fix added afterward.

We do not deliver experimental demos that stay at prototype stage. Everything we deploy has to work in real operation and survive a quality audit, with audit trail, explainability and traceability designed in from the start in GMP environments. If a use case cannot meet that bar, we say so before starting, not after delivery.

We start by looking at what already exists on site: PI System, Wonderware Historian or another source, plus the CMMS failure history. On the pharma maintenance chatbot project, the first phase was a Business Intelligence analysis of over 10 years of CMMS tickets to spot recurring failures, before any predictive model was even discussed. That step is what shows whether a predictive maintenance project has a real chance on your actual data.

The chatbot runs on RAG, Retrieval-Augmented Generation: it does not answer from memory, it draws on the site's technical documentation, maintenance procedures and failure history to build each recommendation. In GMP environments we add explainability and traceability of the sources used, so a technician can check an answer instead of just taking it on faith.

A model running in production drifts over time if nobody watches it: data changes, the process evolves. The MLOps side covers exactly that, tracking the operational reliability of models once deployed, not just their initial tuning. That is what separates a model that gets demoed once from a model that can still be audited a year later.

No. A large part of this service is plain Business Intelligence: Power BI dashboards, and turning analyses still running on critical Excel files or ad-hoc scripts into versioned, auditable tools. A more advanced project like a RAG chatbot or a predictive model usually builds on that groundwork, it does not replace it.