Foundation
The data layer beneath every bioprocess decision.
A unified namespace for every instrument, every historian, every scale. Bioprocess-aware transformation pipelines. End-to-end data lineage. Foundation is what the intelligence layer stands on — the reason every answer downstream is the same answer, no matter who asks, or from which team.
Every instrument, every historian, every system — in one layer.
Invert is a unified namespace for your bioprocess data. Pre-built connections pull from your bioreactors, historians, cell counters, chromatography systems, LIMS, and ELN. Parameters arrive resolved, linked to runs, and ready to use — without custom scripts and without middleware.
Explore integrationsOne metric library — every name, every unit, resolved.
DeltaV calls it “Agitation.” The Biostat STR says “Agitator speed.” Invert maps both to a single canonical metric with standardized units. Your team queries one vocabulary, regardless of equipment source.
Trace how upstream parameters drive downstream outcomes.
See your full process in one view — from bioreactor through centrifugation, hold, and chromatography. Invert tracks stream volumes, step yields, and parameter lineage automatically across every unit operation.
Three steps to a unified data layer.
Connect whatever you already have
Bioreactors, historians, LIMS, ELN, a data lake, Snowflake, an S3 bucket of run exports — we ingest from where your data already lives. OPC-UA, OPC-DA, SFTP, REST APIs, and file upload, all pre-built.
Resolve it into one canonical vocabulary
Bioprocess-aware transformation pipelines map every vendor alias into one shared namespace. “Agitation” and “Agitator speed” become the same metric. Units, formats, and naming conventions are reconciled on the way in.
Every team queries one layer
Scientists get structured, run-centric data. Data engineers get FAIR exports. IT gets a maintainable, enterprise-grade data layer with full audit trails and lineage across every unit operation.
You can get more data direct to the Ambr250 than AVEVA PI can, because you also go file-based. There are temporary files, system logs, events, and more context stored in these log files.
Start with a unified data layer.
Most teams spend more than half their time on data instead of science. A unified data layer changes that in weeks — whether you're starting from paper batch records or layering on top of a mature historian stack.