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.

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See how it works

What enters review

Capture exceptions

unreadable documents, missing fields, supplier resolution

Capture exceptions

unreadable documents, missing fields, supplier resolution

Capture exceptions

unreadable documents, missing fields, supplier resolution

Mapping uncertainty

ambiguous categories, unit conversion doubts, route detection

Mapping uncertainty

ambiguous categories, unit conversion doubts, route detection

Mapping uncertainty

ambiguous categories, unit conversion doubts, route detection

Factor selection

multiple candidates with similar fit, scope boundary checks

Factor selection

multiple candidates with similar fit, scope boundary checks

Factor selection

multiple candidates with similar fit, scope boundary checks

Generated LCAs

new or niche products that need documented assumptions

Generated LCAs

new or niche products that need documented assumptions

Generated LCAs

new or niche products that need documented assumptions

Queues and workflows

Confidence thresholds push items into role specific queues

Specialists review, annotate, and approve with one click

Playbooks guide common fixes and enforce standards

All actions write to an immutable audit log with user and timestamp

Labels and training data

Each decision stores inputs, the human chosen outcome, and notes

A training data pipeline compiles labeled examples for offline evaluation

New models run in shadow mode before promotion

Version notes explain gains and any behavior changes

What we show to customers

How it works

Queue status and SLA stats

Change logs with before and after snapshots

Uncertainty trend lines by category and supplier

Factor specificity mix, share of supplier or product specific LCAs

Controls and safeguards

Role based approvals for sensitive categories and factors

Four eyes rule on generated LCAs before they affect reports

Confidence floors that block low quality changes

Rollback tools to revert a change across the ledger

Download sample report

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

Book a demo

Human in the loop, built in

What enters review

Capture exceptions

Capture exceptions

Capture exceptions

Mapping uncertainty

Mapping uncertainty

Mapping uncertainty

Factor selection

Factor selection

Factor selection

Generated LCAs

Generated LCAs

Generated LCAs

Queues and workflows

Queues and workflows

Queues and workflows

Confidence thresholds push items into role specific queues

Confidence thresholds push items into role specific queues

Confidence thresholds push items into role specific queues

Specialists review, annotate, and approve with one click

Specialists review, annotate, and approve with one click

Specialists review, annotate, and approve with one click

Playbooks guide common fixes and enforce standards

Playbooks guide common fixes and enforce standards

Playbooks guide common fixes and enforce standards

All actions write to an immutable audit log with user and timestamp

All actions write to an immutable audit log with user and timestamp

All actions write to an immutable audit log with user and timestamp

Labels and training data

Each decision stores inputs, the human chosen outcome, and notes

Each decision stores inputs, the human chosen outcome, and notes

A training data pipeline compiles labeled examples for offline evaluation

A training data pipeline compiles labeled examples for offline evaluation

New models run in shadow mode before promotion

New models run in shadow mode before promotion

Version notes explain gains and any behavior changes

What we show to customers

Controls and safeguards

Outcomes

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