Every batch that ever shipped left a trail of certificates and test records. The demand we keep hearing: make that trail answer investigation questions - across batches, generations, plants and sites - without an analyst re-keying values for a week.
Take the certificates of analysis and quality records that already exist and make them answer investigation questions. Compare one parameter across every batch, generation, plant and site. Trace a suspect result back through the test method and the sample to the batch it came from. Know the cost per test, so redundant testing stops hiding in the overhead.
Of all the clusters on this map, quality was the one endorsed without hesitation. The records exist. The questions exist. They have simply never been introduced to each other.
Heard from the leader of an ERP-improvement team at an enterprise coatings and specialty-chemicals maker. Paraphrased, like everything on this map.
COA data is spread across plants, systems and formats. The same parameter shows up under different names and different templates - a table on one site, narrative text on another. Before anyone can compare anything, someone has to make the records comparable, and today that someone is an analyst with a spreadsheet.
The relationships that make an investigation possible - Parameter → Test Method → Sample → Batch - are implicit on paper, and they are lost the moment a document goes into a standard retrieval index.
We do not attach figures to demand signals - directional is honest, and these pools are expensive enough without decoration.
A suspect result means archaeology: open reports plant by plant, find the table, re-key the value, repeat - for weeks.
The lookup becomes a query. Judgment stays with the investigator - the archaeology goes.
Tests get re-run because nobody can see the certificate that already answers them - and nobody knows what a single test costs.
Queryable history plus cost-per-test makes the redundant re-run visible enough to stop.
While a deviation waits for its root cause, stock sits in quarantine - and unsolved causes come back wearing a new name.
Faster trace-back releases stock sooner and retires the repeat offender.
Two engines run in production today - Analytical Lab Reports and account Knowledge Twins. Everything else on this map is an extension on the same foundation.
This entry is the first of those two. Batch-level quality investigation is Analytical Lab Reports scoped to multi-plant investigation: the same parsing that makes Parameter → Test Method → Sample → Batch explicit, pointed at certificates from every plant instead of one lab. The questions above are close cousins of queries it answers in production now.
Read the full use caseAll of it runs inside your infrastructure, within your boundary. Batch data and test results stay under your control - and so does your freedom of action: which models do the reading, where the workload runs, what the economics look like.
Back to the AI ROI MapAnalytical Lab Reports runs in production. Take the sample dataset, run the same investigation queries in the AI tools you already use, and try to prove us wrong. No form, no gate.
On-prem. Your data never leaves your boundary.