Model Governance
How Workplace.io manages changes to automated classification and scoring to support consistency, quality, and customer trust—without
disclosing proprietary model architecture, training data, or internal taxonomy.
Last updated: December 16, 2025
Governance goals
- Consistency: changes should not cause unexpected reporting shifts without clear rationale.
- Quality: evaluation and review help ensure outputs are stable and fit for purpose.
- Auditability: changes are tracked and can be reviewed during customer due diligence.
- Security: inference runs in secured environments with restricted access paths.
Change management
We use controlled change management for updates that affect scoring or reporting outputs. Changes are reviewed, tested, and monitored after
deployment to detect regressions.
- Review: changes affecting analytics are reviewed prior to release.
- Testing: evaluation and targeted testing are performed before rollout.
- Monitoring: post-release monitoring helps detect unexpected behavior.
Evaluation approach
We evaluate updates using internal test and validation processes appropriate to the sensitivity of the change. We intentionally do not
publish proprietary details of training data or signal taxonomy, but we do maintain internal evaluation artifacts.
- Regression checks: ensure changes do not introduce broad quality regressions.
- Stability checks: confirm outputs remain consistent for comparable inputs over time.
- Operational checks: validate inference throughput, error rates, and processing stability.
Access and separation
Inference workloads run on AWS SageMaker and are separated from the application serving layer. Access to inference configuration and
operational credentials is restricted to authorized personnel and systems.
Customer impact and transparency
When changes materially affect how metrics appear, we aim to provide clear, practical explanations in release notes or customer
communications as appropriate to the change scope.