Next-generation precision fermentation: integrating engineered cell factories, bioprocess intensification, and smart bioprocessing

Precision fermentation (PF) uses engineered microbial cell factories to manufacture defined products under controlled bioprocess conditions. In this review, PF is treated operationally as the integration of engineered hosts with metabolic engineering and process control, while traditional food fermentations without engineered production strains are excluded. The field now spans pharmaceuticals, food ingredients, industrial enzymes, specialty chemicals, and biopolymers, but commercial translation remains constrained by biological non-linearity, substrate and feedstock variability, mass-transfer limitations at scale, downstream recovery, and the cost gap with incumbent processes. This mini-review examines five connected layers: engineered microbial chassis; design build test learn (DBTL) and multi-omics-guided metabolic engineering; bioreactor intensification; Process Analytical Technology (PAT), soft sensors and AI-enabled control; and techno-economic and life cycle assessment. Quantitative case studies illustrate both the progress and the remaining scale-up gaps, including semi-synthetic artemisinin, recombinant insulin, lactic acid, succinic acid, QS-21, and polyhydroxyalkanoates. The central argument is that the next phase of PF will depend less on isolated improvements in strain performance than on coordinated integration of strain design, intensified operation, real-time process understanding, and rigorous economic and environmental assessment. This integration is essential for translating laboratory performance into reproducible, scalable, and commercially credible biomanufacturing. Key points PF requires integration of cell-factory engineering with scalable bioprocessing rather than optimisation of any single layer. DBTL and multi-omics improve strain performance, but laboratory titres do not guarantee industrial scalability. Bioprocess intensification and real-time monitoring are required to convert strain performance into robust production. PAT, soft sensors, and AI strengthen process control when supported by reliable online data and qualified models. TEA and LCA should be integrated early to filter commercial and environmental feasibility before scale-up.