Case study

Global textile certification automation

Cross-document verification, multi-country templates, and HITL exception handling powered by session-driven learning.

Context

A certification organization operating across multiple countries with high-volume document bundles and strict compliance requirements.

Components used

• Data House (documents + metadata + indexing)

• Document AI modules (OCR, extraction, normalization)

• Orchestrator (verification workflow + validation gates)

• Sessions & HITL (review queue + corrections)

• Model adaptation (interval training + rewards)

• WebKit (case system updates)

Problem

Manual verification was slow and inconsistent. Small mismatches across documents created rework and increased compliance risk.

Xong approach

Extract and normalize fields, cross-validate bundles, score confidence, route exceptions to HITL, and update systems via tool actions.

Operational improvements designed to scale across countries.

Cross-validation

Automated consistency checks across main docs and subdocs reduce missed mismatches.

Country adaptation

Interval training improves extraction on local templates and languages.

HITL routing

Exceptions go to the right reviewers with evidence and correction UI.

Orchestrated workflow

Durable pipeline with retries, gates, and traceable outputs.

System updates

Structured write-backs into case trackers via WebKit tools.

Document intelligence

OCR + layout + extraction modules tuned for certification formats.

Replace these placeholders with your real KPIs as you measure them in pilots.

Faster cycle time

↓ 30-70%

Illustrative range

Less rework

↓ 20-50%

Illustrative range

Higher throughput per reviewer

↑ 1.5-3x

Illustrative range

Bring a sample case bundle and required outputs. We will propose a traceable workflow with HITL and interval training.