Find the registered ranges your characterization data does not support
An agent that maps every unit operation to its characterization study, compares every proven acceptable range against the range actually registered and operated, and ranks what is missing by how hard it would be to defend.
The process is operating exactly as filed, which is precisely why nobody catches it.
Characterization gets done unit operation by unit operation, over years, by different people, against shifting priorities. Some steps get a full study. Some get a platform justification. Some get nothing because they were never the interesting ones. The record of which is which lives across a study register, a set of individual reports, and whatever the last person remembered.
The gaps this leaves are not deviations. A registered range wider than the range you actually characterized is not a compliance breach, it is a filed range being operated as filed. Nothing alarms. No batch fails. The exposure only appears when an inspector asks what data supports the upper end of a range, or when a batch runs near that end and you have nothing to say about why it is acceptable.
Assembling the honest answer means reading every study against every parameter in the batch record and the filing, and holding several hundred comparisons in your head. It takes two to three weeks, so it happens ahead of a filing or an inspection, not as a standing view.
Every unit operation, every range, against the data that supports it.
The agent is given a product and its characterization workbooks. It walks every unit operation in the manufacturing process, matches each to its characterization study where one exists, compares every proven acceptable range against the range actually registered and operated in the batch record, and returns the gaps ranked by risk: steps with no study at all, registered ranges wider than the data supports, and studies still open against parameters already in routine use.
The finding that matters is the one where nothing is wrong.
An uncharacterized step and an over-wide registered range both look completely normal from inside the process. Every batch passes. No rule is broken. That is why these persist for years, and it is why the check has to be systematic rather than triggered by an event. There is no event.
- The characterization workbooks as PD actually keeps them: the study register, the criticality assessment with its severity and occurrence scoring, the DOE runs behind each study, and the proven acceptable ranges each one established.
- MES for every parameter actually recorded against the process, unit operation by unit operation, so coverage is measured against what is operated rather than against what someone remembered to list.
- PAR against registered range, compared line by line. A registered range narrower than the proven range is conservative and reported as fine. A registered range wider than the proven range means the process may legally operate where no study supports it, and that is the headline.
- Study completeness, because a study that is open at three of twelve planned runs supports considerably less than its entry in the register implies.
- Ranked by consequence, not by count. A viral clearance step with no characterization outranks a formulation step with a partial study, and the output says why in terms a reviewer can argue with.
The agent reports what is characterized, what is not, and where the filed ranges outrun the data. It does not decide which gaps are acceptable, what the remediation plan should be, or whether a range should be narrowed rather than a study extended. Those are process science and regulatory strategy decisions and they stay with your people.
From a pre-filing scramble to a view you can hold all year.
- Study reports read individually against the batch record and the filing
- Coverage depends on someone remembering which steps were skipped and why
- PAR against registered range compared for the parameters people think of
- Done ahead of a filing or an inspection, rarely as a standing view
- Every unit operation and every parameter checked in one pass
- Gaps ranked by consequence, with the reasoning shown for each
- Cheap enough to re-run whenever a study closes or a range changes
- PD decides what to remediate and in what order
These figures come from my demo environment running on synthetic CMC data. They are not client results. The "today" column is what I watched teams actually do across my career; the "with an agent" column is measured on the demo, against a data model built to be representative rather than against your systems.
Development data in spreadsheets is fine. Development data nobody owns is not.
Characterization data lives in working files, and that is normal and workable. The agent reads spreadsheets directly and does not need them migrated first. What stops it is a study register that has drifted from what was actually run, proven acceptable ranges recorded in individual reports but never collected anywhere, or a parameter naming convention in the batch record that shares nothing with the one used in development.
The Data Diagnostic establishes what is reachable and how well the two vocabularies line up, and prices the path from there. Two to three weeks, fixed fee, before you commit to a build that depends on it.
The Data Diagnostic, including the price
Your IT and quality systems group will want the architecture, the read and write paths, the Part 11 position and the validation approach. That is all written down on the governance and validation page, in a form you can forward or print.
Send me a workflow.
Tell me the process that eats your team's week. I'll record an agent running it on your data model and send it back. No call required.
kyle@kylelangham.comNo form, no gate, no email capture.
Would rather talk it through? 30 minutes on your workflow, no pitch.