How confidence scores and bounding-box grounding in ADE support human review workflows that combine automated extraction with targeted field-level validation.
Many regulated document workflows cannot be fully automated: an extracted value affecting a lending decision, a compliance determination, or a clinical action requires human sign-off before reaching downstream systems. ADE's confidence scores and bounding-box grounding make review efficient by routing only the fields that need attention and navigating reviewers directly to the source location.
At production scale, a global Tier-1 bank reduced manual document review time by 40-60% using ADE across 200-300-page multi-lingual KYC packages (bank case study).
The Routing Architecture
Human-in-the-loop document workflows route extracted output on two signals from each field:
- Confidence score. Fields above the set threshold pass to downstream systems automatically. Fields below the threshold route to the review queue.
- Null return. Fields absent from the document route to a missing-data workflow branch, not to the standard review queue.
Confidence scores are returned per field in the extraction metadata. Threshold values are pipeline configuration and should be calibrated against a representative sample of production documents before go-live.
What Reviewers See
For each field routed to review, the interface presents the extracted value, the confidence score, and the source location from the chunk_references in the extraction metadata. With bounding-box citations, the interface can display the source region of the original document alongside the extracted value, reducing review to a confirm-or-correct action rather than a full document read.
Schema Design for Reviewable Outputs
The extraction schema affects reviewability. Fields extracted as typed primitives (string, number, date) are straightforward to display and correct in a review interface; fields extracted as nested objects or arrays require an interface that can render and edit complex structures.
Flattening arrays into separate fields during extraction reduces the interface complexity required for the review step.
Audit Trail After Review
After a reviewer confirms or corrects an extracted field, the review action should be logged alongside the original extraction output: original extracted value, confidence score, reviewer identity, correction if any, and timestamp. Combined with ADE's bounding-box grounding, this creates a complete audit trail from source document through automated extraction through human review to final value.
For compliance workflows, the Trust Center documents the data handling controls, SOC 2 Type II and Zero Data Retention, that govern the extraction step of this chain.
FAQ
How should confidence thresholds be set for routing to human review? Calibrate thresholds against a representative sample of production documents before go-live, setting the threshold at the confidence level above which extraction errors are acceptable to the downstream system. Alert when the proportion of low-confidence fields exceeds baseline, which may indicate a document format change or schema issue.
What information should a review interface display for each routed field? At minimum: the extracted value, the confidence score, and the source location from the bounding-box citation. Rendering the source document region enables visual confirmation without navigating to the source document manually; this is the difference between a review task that takes seconds and one that takes minutes.
How does null handling affect the review routing logic? Null returns mean the field is absent from the document, not that extraction failed. Routing null returns to the standard review queue alongside low-confidence fields conflates two different conditions; null returns should route to a missing-data workflow instead. Null handling behaviour varies by model version; see extraction model versions for current behaviour.
Does ADE support webhooks to trigger review workflows when low-confidence fields are detected? ADE returns extraction output synchronously via the Parse API or via polling via the Parse Jobs API; the confidence-based routing logic lives in the calling pipeline, which examines each field's confidence score after extraction and routes accordingly.
Can review corrections be fed back to improve future extraction accuracy? ADE does not currently provide a feedback mechanism for routing reviewer corrections back to the extraction model. Human review corrections should be logged in the calling application and can inform threshold calibration and schema refinement decisions, but they do not directly update ADE's extraction behaviour.