quality_assurance · public · workflow
BQA streamlines education quality reporting using Amazon Bedrock and generative AI
BQA's manual review of self-evaluation reports was slow and error-prone: institutions submitted incomplete or inaccurate information, supporting evidence frequently failed to substantiate claims, and staff spent significant time and resources following up with institutions to rectify submissions.
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 · Document upload to S3
Relevant documents are uploaded and stored in an Amazon S3 bucket.
Tools used
Amazon BedrockAmazon SageMaker JumpStartMeta Llama
Outcome
BQA's proof of concept with Amazon Bedrock anticipates faster turnaround times for generating 70% accurate and standards-compliant reports, a 30% reduction in evidence analysis time, and 30% reduced operational costs through process optimizations.
Results
Time saved30%
Volume70%
Cost replaced30%
Grounding & classification
Source type: platform led case
25 fields verified against source quotes, 7 dropped as unverifiable.
data extractiondocument aiidpsummarizationform submissionpolicy documentmetric backednamed customerworkflow describededucationgovernmentaccuracy improvementcost reductionerror reductiontime savedplatform led casecompliance monitoringquality assuranceregulatory reportingdocument to recordextract classify route