Six capabilities that connect to your existing DeltaV or Siemens SIMATIC control systems, run against your own historical run data, and deliver deviation alerts and batch documentation into the ELN your engineers already use — without a parallel monitoring application.
Fermentile builds a statistical golden batch model from the CDMO's historical run records for each product-organism-bioreactor combination. The model captures multivariate process trajectories — pH, DO, temperature, agitation, metabolite profile — and their allowed variance envelopes at each phase of the fermentation run. During an active batch, the model continuously compares the live sensor stream against the golden trajectory and generates a probabilistic deviation score updated every five minutes.
The alert engine is trained to identify the multivariate sensor patterns that historically preceded yield failures in the CDMO's own run history. When a pattern match is detected, the engine generates a structured alert specifying the deviation category, the confidence level based on historical pattern frequency, and a ranked list of corrective actions derived from batch records where the same deviation was successfully corrected. Alerts are delivered to the process engineer's ELN within a 15-minute response window.
Scale-up transfer failures are among the most costly events in a CDMO's operations. Fermentile's scale-up module takes the golden batch model from the development scale and applies engineering correlations and a regression model trained on the CDMO's historical scale-up transfer records to predict the parameter adjustments most likely to reproduce the development yield at the target manufacturing scale. The output is a scale-up run sheet the process team can use before the first manufacturing run.
Regulatory-grade batch records require complete traceability of every process parameter, deviation event, and operator intervention across a fermentation run. Fermentile automatically assembles a draft batch record from the live sensor stream, alert history, and operator notes entered in the ELN, pre-formatted for the CDMO's existing batch record template. The draft captures phase timestamps, deviation events with their alert and response records, parameter setpoint changes, and final yield measurement. Batch record preparation time from 4-8 hours to under 1 hour per run.
CDMOs run multiple client programs simultaneously, each with different organisms, media formulations, and target products. Fermentile's process library maintains isolated model instances per product-organism-bioreactor combination, ensuring that deviation training data from one program does not contaminate the model for another. Process engineers can view, compare, and export golden batch trajectories across programs from a single dashboard.
Fermentile's yield prediction model ingests mid-run metabolite readings, growth curves, and deviation event history to forecast the final batch yield range 24-48 hours before the scheduled harvest time. The prediction is displayed as a confidence interval alongside the yield target specified in the batch record. CDMOs can use the forecast to manage client communications when a batch is tracking below target, plan downstream processing capacity, and decide whether a corrective intervention is warranted before harvest.
Fermentile connects to the systems CDMOs already operate. No new hardware, no proprietary sensors, no data lake project before the platform goes live.
Benchling ELN
SciNote
Emerson DeltaV
Siemens SIMATIC PCS 7
OPC-UA Data Gateway
LabVault LIMS