Case Study | CMC Biologics Development
3,000+ FTE hours recovered per program.
A bioprocessing organization running upstream and downstream CMC programs was losing over 3,000 FTE hours per program to manual data work. They deployed Invert starting with file-based ingestion alone, no changes to validated systems, and compressed that burden to under 75 hours per program.
About the Company
This bioprocessing organization runs upstream and downstream CMC programs for mAb development. A single fed-batch run generates weeks of online time-series data alongside daily offline measurements, plus downstream analytical results for titer, glycosylation, and charge variants. Before any of that data could inform process characterization studies or deviation investigations, it had to be assembled manually — a direct cost on margins and timelines.
The Challenge
The team pulled experimental data from five to six separate systems before analysis could begin: bioreactor exports, daily offline sample logs, analytical results from HPLC and osmometry, and batch metadata from their ELN. Reconciling them into a usable dataset required manual effort per batch. Three problems compounded across the program lifecycle.
A data problem hiding inside a science problem
Matching data to the correct program, batch, process version, and scale required manual work every time. Normalizing parameter naming conventions, matching timestamps, and setting up a visualization environment consumed 40 hours of configuration before a single chart could be produced.
Process characterization bottlenecked by data assembly
PC studies require cross-batch comparisons, multivariate analysis, and traceability from raw run data to conclusions about product quality. When the underlying data infrastructure is manual, report generation, deviation investigations, and root cause analysis each add days of overhead before actual scientific work can begin.
Cost compounding across the program lifecycle
Beyond individual batches and PC studies, the manual burden accumulated at every stage: retrospective analyses for scale-up decisions, tech transfer documentation, and cross-program comparisons. Each activity represented time scientists could spend advancing programs rather than assembling data.
How Invert Solved It
The deployment began with automated ingestion of the data types that define upstream CMC work: bioreactor exports, daily offline sample logs, and post-batch analytical results. Invert's configurable file ingestion mappings handled reconciliation automatically — matching data to the correct batch context, normalizing parameter names, and storing everything in a centralized, batch-centric database.
Automated ingestion across all data sources
Bioreactor exports, offline sample logs, and analytical results ingested automatically. Data matched to the correct batch context from the ELN, normalized across instruments, and tagged by batch ID, process version, scale, and program.
From weeks of assembly to instant access
Post-batch data aggregation dropped from 20 hours to minutes. Visualization setup that consumed 40 hours per batch became instant. Scientists could compare runs across scales and process versions without assembling a single spreadsheet.
Deviation closure accelerated
With a complete, structured batch history instantly searchable, deviation investigations start from context rather than from a data retrieval exercise. Closure timelines compressed by 90%, reducing both investigation cost and timeline impact.
Invert Assist
PC reports with AI, without the manual construction
With structured, queryable batch data available, the team generates process characterization reports directly from accumulated run data using Invert Assist. Using an existing PC report as a template, Assist drafts new reports complete with cross-batch plots, statistical summaries, and parameter trend analyses. Scientists review and refine rather than build from scratch.
Report generation time dropped 98%. Assist surfaces conclusions that were not specifically asked about but were impactful to the process — turning what would have been days or weeks of manual work into a five-minute analysis.
Results
Per-batch data prep time reduced from 106 hours to 1.7 hours.
1.7 to 2 FTEs recovered annually per program — time redirected from data assembly to science.
Historical data retrieval reduced from days to seconds.
Deviation closure timelines compressed by 90%, reducing both investigation cost and client program impact.
PC report generation dropped 98% — scientists review and refine rather than build from scratch.
The system was picking up and reporting on conclusions that were not specifically asked about, but were impactful to the process. If I had to do all this work myself, it could have taken days or weeks. The analysis took five minutes.