Quality assurance · Production

Anomalo solves unstructured data quality issues to deliver trusted AI assets with AWS

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Documents stored in Amazon S3
trigger
“PDF files, PowerPoint presentations, and Word documents stored in Amazon Simple Storage Service (Amazon S3)”
2
Automated OCR and text parsing
ai_action
“Anomalo automates OCR and text parsing for PDF files, PowerPoint presentations, and Word documents stored in Amazon Simple Storage Service (Amazon S3) using auto scaling Amazon Elastic Cloud Compute (Amazon EC2) instances, Amazon Elastic…”
3
Continuous anomaly detection
validation
“Anomalo inspects each batch of extracted data, detecting anomalies such as truncated text, empty fields, and duplicates before the data reaches your models. In the process, it monitors the health of your unstructured pipeline, flagging s…”
4
Governance and PII compliance
validation
“Built-in issue detection and policy enforcement help mask or remove PII and abusive language. If a batch of scanned documents includes personal addresses or proprietary designs, it can be flagged for legal or security review”
5
LLM-powered document quality analysis
ai_action
“Anomalo uses Amazon Bedrock to give enterprises a choice of flexible, scalable LLMs for analyzing document quality”
6
Validated data delivered to AI apps
output
“The validated data layer provided by Anomalo and AWS Glue helps make sure that only clean, approved content flows into your application”
Reported 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.

Reported metrics
Data quality problem resolution timein minutes instead of weeks
Development time savingssave months of development time
generative AI project abandonment rate (Gartner industry estimate)30%
share of enterprise data that is unstructured (MIT Sloan industry estimate)over 80%
Reported stack
AnomaloAWS GlueOCR
Source
https://aws.amazon.com/blogs/machine-learning/how-anomalo-solves-unstructured-data-quality-issues-to-deliver-trusted-assets-for-ai-with-aws?tag=soumet-20
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 tools did this team use?

Anomalo, AWS Glue, OCR.

What results were reported?

Data quality problem resolution time: in minutes instead of weeks; Development time savings: save months of development time; generative AI project abandonment rate (Gartner industry estimate): 30%; share of enterprise data that is unstructured (MIT Sloan industry estimate): over 80% (source-reported, not independently verified).

What failed first in this deployment?

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.

How is this quality assurance AI workflow structured?

Documents stored in Amazon S3 → Automated OCR and text parsing → Continuous anomaly detection → Governance and PII compliance → LLM-powered document quality analysis → Validated data delivered to AI apps.

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