Built by people who understand what happens when an audit trail review fails.
XceptionIQ exists because regulated labs deserve better tools. Not just faster tools — smarter, more defensible ones that amplify QA expertise instead of replacing it.
Our Story
The problem was right in front of us.
Audit trail review is one of the most compliance-critical activities in a regulated lab — and one of the most manually intensive. QA teams were spending days reviewing thousands of event rows, building spreadsheets, and writing exception reports by hand.
The tools available were either generic document viewers, basic keyword search utilities, or expensive LIMS modules that didn't understand the audit review workflow. None of them understood what an exception actually was.
We believed there was a better way — one that used AI to surface patterns, kept humans firmly in control of every decision, and produced a fully defensible audit trail of the review process itself.
XceptionIQ is that better way.
Precision over coverage
One missed critical exception is worse than ten flagged low-severity items. We optimise for finding what matters, not for volume of findings.
Human judgment is irreplaceable
AI is our detection engine. QA reviewers are the decision makers. We don’t let the platform claim a finding is compliant or non-compliant without a human in the loop.
Defensibility is not optional
Every AI action, every reviewer decision, every approval is logged with timestamp, user, model version, and rationale. Because the next question from an inspector will always be “show me how you found that.”
Complexity must be absorbed by the platform
Six instrument families, each with different event vocabularies, schemas, and context requirements. That complexity belongs in the engine — not in the reviewer's head.
Our Mission
To give every regulated laboratory team complete confidence that their audit trail reviews are thorough, defensible, and free from the gaps that only emerge when a human reviewer is overwhelmed by volume.
Our Vision
A world where no pharmaceutical batch failure, warning letter, or patient safety event is traced back to a missed audit trail exception — because the tools exist to find every one.
Design Principles
The rules we never break
Rows participate in patterns
Exceptions are never flagged because of a single suspicious word. They are flagged because a set of rows matches a policy pattern and expected context is missing.
AI suggests, humans confirm
The platform will always present findings as candidates requiring review. It will never declare a finding compliant or non-compliant without a reviewer decision.
Evidence must be traceable
Every candidate exception must cite the rows that constitute its evidence — trigger event, supporting events, contradicting events, and any absence conditions.
Instrument complexity is internal
Reviewers see one unified review experience. All per-instrument normalisation, adapter logic, and context mapping happens invisibly in the pipeline.
Nothing is final without approval
Critical findings require supervisor sign-off before they appear in a released report. No automated shortcut bypasses the human approval gate.
The review itself is audited
What the AI saw, what version of the model it used, what the reviewer decided, and when — all of it is immutably stored alongside the exception record.
Who We Serve
Regulated industries where data integrity isn't optional
Pharmaceutical Manufacturing
GMP compliance, batch release, deviation management
Biotech & Cell Therapy
Complex process data, multi-instrument workflows
Medical Device
21 CFR Part 820, ISO 13485 documentation
Contract Research Orgs
Multi-client audit trails, cross-study compliance
Want to learn more or see a demo?
We'd love to show you XceptionIQ in action on your instrument type.