You review a sample. You answer for all of it.
Visium builds the evidence layer around the quality system. Deviations classified and prioritised at the moment they happen, CAPA effectiveness measured against what recurred afterwards, audit evidence assembled from the record with the page behind every claim. Every output carries the document, the version and the effective date it came from, so the weeks before an inspection go to the answer instead of the search.
The compliance evidence problem
Most of what an inspection costs is spent before the inspector arrives. Weeks of assembling evidence that already existed, for questions that were reasonable and could not have been predicted.
Reviewed in samples, accountable in full
QA reads a fraction of what the quality system produces. The deviations, complaints, lab results and supplier records that were never sampled still belong to you at release and at inspection. A pattern that appears only across the full set stays invisible in a sample.
The evidence exists. The answer has to be built.
What an auditor asks for already sits in the QMS, the eDMS, the LIMS, the ERP, the training system and the threads where the reasoning actually happened. Each of those was built for its own purpose. The question is new every time, the assembly is manual every time, and one answer costs a person a week.
Actions close. Failure modes continue.
A CAPA closes on a date. Whether the failure mode stopped recurring is a separate question, answerable only by reading across events that nobody reads across. Repeat deviations are the visible part of this.
The requirement moves faster than the document estate
Guidance is revised. An inspection finding lands at a peer. A supplier changes scope. Each of those leaves a set of SOPs, validation files and agreements saying the wrong thing. Finding that set is manual work, so it happens late.
And the person who decides whether it holds was not there
Every judgement in this value stream is re-read by someone outside it. A QP at release, a corporate auditor, a notified body, an inspector who opens the file at the page you did not expect. A conclusion that cannot be reconstructed from the record does not count as a conclusion. Provenance is where this build starts.
Ten quality workflows. We start most engagements with three of them.
These three carry the clearest cost of delay and the shortest path to a measurable result.
Deviation and non-conformance triage
Events are captured at source and classified against your own history, then ordered by the risk they carry to release rather than by when they arrived. The ones that need a full investigation become visible on day one.
A queue ordered by impact, each event carrying comparable prior events and a proposed classification, and a named list of what the initial report leaves out.
CAPA drafting and effectiveness
Actions are drafted from the investigation and from what worked in comparable cases. After closure, the system keeps watching for the same failure mode, so effectiveness becomes something you measure rather than something you assert.
Drafted CAPAs with precedent attached, effectiveness tracked against subsequent events, and actions that closed without supporting evidence identified.
Audit and self-inspection preparation
Ask the estate the questions an auditor will ask, before an auditor asks them. Every batch of a product, every change to a process, every training record behind a signature, assembled with the documents attached.
An evidence pack per question, each claim linked to the document, version and effective date, and a named list of what the estate does not contain.
Quality trend analysis
Read deviations, complaints, OOS events and change controls across the full set, grouped by failure mode rather than by the category selected at intake.
Complaint intake and classification
Read free-text complaints for the signal the category field never captured, and classify at intake against your own coding conventions.
Document control and periodic review
Find every controlled document a change touches, surface contradictions and references to versions that no longer exist, and hold periodic review to its schedule.
SOP authoring and harmonisation
Draft and align procedures across sites, with the differences between site versions named and the reason behind each one visible.
Supplier quality monitoring
Read supplier documentation and performance against your requirements, and show where the evidence behind a qualification is thin.
QC lab data review
Check analytical results against specification, method and prior runs, with out-of-trend results surfaced alongside out-of-specification ones.
Training-record compliance
Find where a procedure changed and the training behind it did not, by person, site and role.
Regulatory document authoring
including APQR and authority responses, runs on Scribe using this same evidence.
What we bring to a discovery programme
Discovery teams do not need another general model. They need scientific judgement encoded in something reviewable, running on their own data, inside their own environment.
Eight years on one problem
We have been building AI for pharma companies since 2018. Devin exists because this workflow was worth productising, and the version you see now carries what we learned in real facilities.
Validation is the first design constraint
Your quality function sees the intended use, the risk assessment and the test approach before development starts. QA is the user in this value stream, so their acceptance criteria shape the build from the beginning.
We measure whether it still works after go-live
Validation proves a system performed on the day it was tested. Formats change, language moves, and accuracy follows quietly. We build the monitoring that makes that drift visible and reportable, so you can state your system's current performance whenever someone asks for it.
Every output carries its source
Each flag, score and drafted line links back to the document, the version and the page. In our pathology work a reviewer sees the extracted value highlighted in the original report and confirms it in seconds. Audit trails on agent activity, role-based access and GxP-compliant model versioning come as part of the build.
Quality decisions with the data behind them
Discovery buyers ask for validated performance, published methods and named collaborations. Here is what we have on record.
%20(1).webp)
%20(1).webp)
.webp)