The warning sign was there. It just wasn't being read.
Why API Log Analytics exists, and what we believe every manufacturing site owes its own batch records.
Seven years as a process engineer, filing records that nobody analyzed.
Dr. Imogen Farrow spent seven years as a process engineer at an Active Pharmaceutical Ingredient manufacturing site, generating exactly the documentation cGMP requires — batch records, deviation reports, out-of-specification investigations — and watching almost all of it get filed, reviewed for individual completeness, and then never looked at again as a dataset.
A year of data, and a pattern nobody had connected.
Following an FDA inspection that cited a data integrity observation, she was asked to pull a full year of deviation records for the affected line during the response effort. Laid out together, month by month, the drift was unmistakable — a slow shift in a critical process parameter that had been individually explainable in every single deviation report and collectively obvious the moment someone actually plotted it over time. Nobody had been negligent. Nobody had been plotting it over time.
She founded API Log Analytics in 2016 with a specific premise: the record-keeping cGMP requires is also, if anyone bothers to look, one of the best early-warning systems a manufacturing site already has. Most sites are just generating it for the wrong reason and never turning it around to look backward.
Yes, we know what most people think API means.
We considered a name that would avoid the confusion entirely. We kept this one because the confusion is, in a small way, the point: most software gets more systematic monitoring attention than most pharmaceutical manufacturing processes do, and we think that's worth people noticing, even for a second, before they realize what industry they've actually landed on.
Four things that haven't changed since 2016.
We report trends, never conclusions about cause.
Our analysis identifies statistically significant patterns. Determining root cause and regulatory impact stays with your quality unit, where the accountability actually sits.
Data integrity findings get flagged immediately, not batched.
If we find something that looks like an ALCOA+ violation during a review, your quality team hears about it the same day — not in a summary report weeks later.
We work inside your validated systems, not around them.
Every analysis respects your existing LIMS, MES, and electronic batch record validation status. We don't ask you to export data into an unvalidated shadow system to get value from it.
A dashboard nobody checks isn't a deliverable.
Every CPV dashboard we deploy comes with a defined review cadence and a named owner on your side — otherwise it's just one more report joining the ones that already go unread.
Meet the team applying this daily: Our Team, or see exactly how an engagement starts on the How It Works page.