back_office_ops · saas · workflow
Beekeeper optimizes LLM selection and user personalization with an Amazon Bedrock-powered dynamic evaluation system
Organizations face a moving target when selecting and maintaining LLMs: the best model and prompt combination shifts as models, prices, and requirements change, and most mid-sized companies lack the resources to continuously evaluate and improve them.
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 · Scheduler triggers coordinator
A scheduler triggers the coordinator, which fetches test data and sends it to evaluators.
Tools used
Amazon BedrockAmazon EventBridgeAmazon Elastic Kubernetes Service (EKS)AWS LambdaAmazon Relational Database Service (RDS)Amazon Mechanical TurkConverse APIAmazon NovaAnthropic Claude 4 SonnetMeta Llama 3Mistral 8x7BMistral LargeQwen3
Outcome
Beekeeper's system delivers 13–24% better ratings on responses aggregated per tenant, reduces manual labor in LLM and prompt selection, shortens the feedback cycle, and enables user- and tenant-specific prompt improvements.
Results
Volume13–24% better ratings on response when aggregated per tenant
Cost replacedaround $48
Grounding & classification
Source type: technical build writeup
34 fields verified against source quotes.
agentic workflowcontent generationpersonalizationsummarizationchat transcriptbuilder submittedhuman review describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedsoftwareaccuracy improvementemployee productivitytechnical build writeupback office opsextract classify route