Executives need to make data-driven decisions during live business reviews, where accuracy and speed matter. A conversational agentic AI assistant can meet this need by answering data questions instantly. But the stakes are high: a wrong number or a slow response in front of leadership carries immediate professional consequences, and a capable large language model (LLM) alone can’t guarantee either. In production, that gap shows up as hallucinated metrics, API throttling, validation latency, and subjective language. Closing it requires production-ready quality assurance built into every step, from data retrieval to response delivery. This post details five techniques, implemented on Amazon Bedrock, that work together to deliver it: adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, composite evaluation framework, and data accuracy verification. Each addresses a distinct failure mode while operating as a coordinated…
NarrateAI: production-ready LLM quality assurance on Amazon Bedrock | Amazon Web Services
