How ASAPP Uses Amazon Bedrock to Achieve 91% First-Call Resolution

ASAPP operates at the intersection of AI and enterprise customer service, building a GenerativeAgent Platform that orchestrates five to seven large language models simultaneously to handle voice and digital interactions end-to-end. Its clients include some of the world’s largest organizations, where marginal improvements in contact center efficiency translate directly to significant operational savings and measurable customer satisfaction gains.

The platform’s early architecture included a homegrown PII redaction layer built to protect sensitive customer data, but this came at a steep cost. The system slowed interactions, degraded response quality, and prevented GenerativeAgent from accessing the contextual information it needed to resolve complex requests. Rebooking an airline ticket, for example, required address data that the redaction system stripped out, forcing workarounds or immediate human escalation.

ASAPP transitioned from its previous AI provider to Amazon Bedrock, gaining access to Anthropic’s Claude Sonnet models alongside AWS’s built-in enterprise data privacy controls. The GenerativeAgent Platform now routes interactions dynamically across its LLM ensemble depending on use case, latency requirements, and channel (voice or chat). Crucially, the homegrown PII layer was retired entirely, as Amazon Bedrock’s compliance infrastructure met enterprise standards without the performance penalty.

The results were immediate and measurable. GenerativeAgent resolved up to 40% more issues without escalating to human agents. ASAPP’s enterprise clients saw a 49% increase in customer self-service engagements, a 77% reduction in cost per chat interaction, and a first-call resolution rate of 91% for complex service issues. Human agents simultaneously gained the capacity to handle three times as many complex interactions because routine volume was absorbed entirely by AI.

ASAPP is now working with AWS to test Amazon Nova Sonic, a speech-to-speech model delivering near-real-time voice conversations. The company’s trajectory signals a broader shift across enterprise contact centers: from menu-driven IVR systems to AI agents so capable that customers routinely ask whether they’re talking to a human. For organizations processing millions of interactions at scale, this has moved from pilot to critical infrastructure.