Solution
For quality and manufacturing operations teams in pharma: batch genealogy, record validation, and SOP change tracking answered in plain English from the documents your plant already produces.
The problem
Traceability questions were logged and chased manually across systems, and answers took days — with each increase in production volume making the workflow slower, not just busier.
Raw material and product traceability data sits scattered across spreadsheets, PDFs, and scanned records, so every genealogy question becomes hours of cross-referencing.
Frequent SOP updates with no automation behind them leave batch documentation trailing the current procedure — the kind of drift an auditor finds before you do.
The product, not a promise
How it works
Batch records, SOPs, spreadsheets, PDFs, and scans flow in from across manufacturing systems.
AI agents extract and structure the data locked inside each format.
Genealogy agents map raw materials, intermediates, and finished product across datasets into flowchart views.
Agents validate records, track SOP changes, and flag compliance risks as they appear.
Teams ask questions in natural language and get answers as text, tables, or visuals — with sources.
Who it's for
Quality engineer
Site quality head
Compliance / regulatory affairs
A US-based global pharmaceutical company — biologics, advanced therapies, and small-molecule drugs — was tracking 36,000 batch queries by hand. Raw material and product traceability suffered, answers took days, and the data needed to answer them was scattered across Excel sheets, PDFs, and scanned records. Frequent SOP updates with no automation behind them meant documentation drifted out of sync, and every increase in production volume made the manual workflow slower, not just busier.
In oncology manufacturing, traceability and documentation accuracy sit directly on the patient-safety path — the cost of drift is measured in more than hours.
Botminds deploys AI agents against the documents and data the plant already produces. The agents read spreadsheets, PDFs, and scans, extract the batch and quality data inside them, and keep it queryable. Teams ask questions in natural language — no SQL — and get answers back as text, tables, or visuals, generated through AI-driven query automation.
For genealogy, agents map raw materials, intermediates, and finished product across datasets and render the lineage as flowchart insights, replacing hours of manual cross-referencing per query. For change control, agents validate records, track SOP updates, flag compliance risks, and generate trend reports so quality teams see drift before an auditor does.
Pharma quality systems run on evidence. Every answer the agents produce is traceable to the record it came from, and compliance-relevant findings route to a human for the call — the agents surface, people decide. Automation does the reading and cross-referencing at scale while accountability stays with the quality organization. The outcome is faster batch queries, consistent SOP documentation, and traceability that holds up as production scales.
Objections, answered
Every answer is traceable to the record it came from — the batch sheet, the SOP version, the scanned page. Compliance-relevant findings route to a human for the call: agents surface, the quality organization decides.
That mix is the design case. Agents extract and structure data from spreadsheets, PDFs, and scanned records into one queryable layer, so a genealogy question spans all three formats in a single answer.
The platform assists the quality system rather than replacing it. Answers carry their evidence, findings route to named humans, and the full trail — question, sources, decision — is preserved for inspection.
Point the platform at a set of batch records and SOPs; the first genealogy and validation answers are a review exercise against known cases. Most quality teams are checking agent answers against their own within days and using them in live queries within weeks.
Watch agents trace it from raw material to finished product, with sources, live in the demo.
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