Miro uses Amazon Bedrock to boost bug routing accuracy and reduce time-to-resolution from days to hours
Miro's engineering organization suffered from high rates of bug misrouting, causing repeated reassignments, SLA misses, and an estimated 42 years of cumulative lost productivity annually. Traditional NLP classifiers degraded quickly when organizational structures changed.
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 · User submits bug in Slack
A user posts a feedback report into a dedicated Slack channel, optionally including text and media attachments such as screenshots or screen recordings.
Tools used
Amazon BedrockAmazon Bedrock Knowledge BasesAmazon Nova ProAnthropic's Claude Sonnet 4Amazon OpenSearch ServerlessAmazon EKSAmazon S3ConfluenceGitHubBackstage
Outcome
BugManager achieved a six-fold reduction in team reassignments and a five-fold improvement in median time-to-resolution, transforming what once took days into an hours-long process. Top-1 routing accuracy exceeded 75%—a 70% increase over the prior solution—with top-3 accuracy reaching 95%.
What failed first
An existing fine-tuned GPT model showed quickly degrading performance and required retraining whenever teams merged or responsibilities changed, making it impractical for Miro's dynamic engineering organization.