quality_assurance · workflow
Anomalo solves unstructured data quality issues to deliver trusted AI assets with AWS
Enterprise AI projects frequently fail due to unstructured data quality problems—unreliable OCR extraction, PII compliance risks, and incomplete or duplicative content—compounding a manual review process that is too slow and error-prone to scale to production AI requirements.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Documents stored in Amazon S3
PDF files, PowerPoint presentations, and Word documents stored in Amazon S3 serve as the pipeline's source data.
Tools used
AnomaloAWS GlueOCR
Outcome
Anomalo detects and addresses unstructured data quality problems in minutes instead of weeks, saves months of development time, and ensures only clean, compliant content flows into AI applications.
What failed first
Existing manual document analysis processes are not efficient or accurate enough to meet modern business needs, relying on staff review that cannot scale to enterprise document volumes within budget constraints.
Results
Time savedin minutes instead of weeks
Volume30%
Grounding & classification
Source type: platform led case
27 fields verified against source quotes, 7 dropped as unverifiable.
anomaly detectiondata extractiondocument aiocrragcontractsocial media postsupport ticketmetric backedsource backedvendor confirmedworkflow describedcost reductioncycle time reductiontime savedplatform led casecompliance monitoringdata entry opsquality assuranceextract classify routemonitor detect alert