10180738EcoSynth: Enabling Reliable Data CaptureActiveGrant for R&DInnovate UKThis project will develop a new **AI-based anomaly-detection and data-assurance framework** to improve how cleanroom manufacturing environments are monitored and managed. The work is a collaboration between **Decision Lab**, a UK specialist in digital decision intelligence, and the **Cell and Gene Therapy Catapult (CGTC)**, which operates advanced biomanufacturing facilities that support the UK's growing cell and gene therapy sector. Modern cleanrooms rely on hundreds of variables -- such as temperature, pressure, humidity, air flow, and equipment activity -- but only a small fraction of process steps are routinely logged. This makes it difficult to identify which part of a complex production sequence is causing reduced yield, quality deviations, or excess energy use. Engineers can often see that one room is performing worse than others, yet must manually review every process step to find the cause. The project will design an algorithm that learns from the performance of multiple cleanrooms to detect when one deviates from normal behaviour and to **pinpoint the most likely step or parameter responsible**. By ranking potential root causes, the tool will help engineers target investigations quickly and confidently. It will also generate a **data-quality index**, highlighting where information is incomplete or unreliable, and will log all results in a transparent, regulator-aligned format suitable for Good Manufacturing Practice (GMP) environments. This innovation is important for two reasons. First, it strengthens the UK's advanced therapy manufacturing capability by giving operators a faster, evidence-based method to diagnose issues and maintain consistency across facilities. Second, it creates the **trusted data foundation** needed for future digital optimisation tools, including Decision Lab's _EcoSynth Orchestrator_---an AI system designed to improve energy efficiency and sustainability across medicines manufacturing. The approach combines CGTC's operational insight and real manufacturing data with Decision Lab's expertise in artificial intelligence, optimisation, and data assurance. Together, the partners will demonstrate how AI can improve reliability, traceability, and sustainability without disrupting GMP compliance. The project contributes to the UK's ambition to make medicines manufacturing smarter, cleaner, and more resilient. By establishing a robust, auditable framework for AI-driven data reliability, it lays the groundwork for more sustainable and efficient biomanufacturing across the country.