Bardo do the work to understand what was bought
Human in the loop, built in
Everything is monitored by specialists. Every corner case or uncertainty is flagged, reviewed, and corrected. Decisions feed a training data store that improves the models and lowers uncertainty over time.
What enters review
Labels and training data
Decision memory
Each decision stores inputs, the human chosen outcome, and notes
Training data pipeline
A training data pipeline compiles labeled examples for offline evaluation
Shadow mode validation
New models run in shadow mode before promotion
Version transparency
Version notes explain gains and any behavior changes
What we show to customers
Coverage & reconciliation
Factor quality & specificity
Uncertainty & confidence
Factor specificity mix, share of supplier or product specific LCAs
Controls and safeguards
Approvals for sensitive categories and factors
Human eyes on generated LCAs before they are used
Confidence floors that block low quality changes
Rollback tools to revert a change across the ledger
Outcomes
Higher quality on day one, less rework at audit
Better models each month, lower uncertainty over time
Transparent operations that build trust with finance and auditors
