quality_assurance · saas · workflow
QyrusAI and Amazon Bedrock power shift-left testing with 80% reduction in defect leakage
Traditional testing methods occurring late in the development cycle often result in delays, increased costs, and compromised quality, making it difficult for businesses to maintain rigorous standards while accelerating development.
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 · Upload requirements document
A user uploads a sample requirements document to begin the testing workflow.
Tools used
QyrusAIAmazon BedrockMeta's Llama 70BAnthropic's Claude 3.5 SonnetCohere's English EmbedPineconeAmazon ECSAmazon S3Amazon EFSAWS LambdaClaude 3 OpusClaude 3 SonnetMeta's Llama 3.1Application Load Balancer
Outcome
Early adopters of QyrusAI saw an 80% reduction in defect leakage, a 20% reduction in UAT effort, and a 36% faster time to market.
Results
Time saved36% faster
Volume80%
Grounding & classification
Source type: technical build writeup
37 fields verified against source quotes, 1 dropped as unverifiable.
agentic workflowai agentcomputer visioncontent generationdocument aimulti agent workflowragknowledge basebuilder submittedmetric backedproduction runtime claimedtools describedworkflow describedsoftwarecycle time reductionerror reductiontime savedtechnical build writeupquality assuranceagentic task execution