OMNICHANNEL CONVERSATIONAL SEARCH: MAINTAINING CONTEXT AND CONSISTENCY ACROSS VOICE AND WEB INTERFACES

This research work presents an omnichannel conversational search platform that provides consistent, context-rich responses via voice and web channels. Current conversational agents address voice and web channels separately, leading to fragmented user experiences and context fragmentation between cross-channel interactions. Our platform, which is built on Dialogflow CX and Google Cloud, enables a single conversational agent to support Telephony Gateway voice calls and Web/Messaging requests. For context and to deliver earthed answers, we employ a Cloud Run tool-router for conversation-aware query rewriting and retrieval with Vertex AI Search/Embeddings with Matching Engine, including span-level citations. Scoped session memory is stored in Firestore with DLP redaction to preserve privacy while permitting shared context between channels. Apigee X enforces operation limits and circuit breakers, Cloud Tasks manages delayed enrichment, and BigQuery with Cloud Trace/Logging supports online and offline evaluation. A channel-consistency controller for synchronized answers across modalities, a handoff linker for state preservation during voice-to-web handoffs, and a latency/cost-aware cache enhancing sub-second FAQ answers are at the core of these. System performance is quantified using metrics like consistency of responses, groundedness, deflection ratio, session continuity after channel switch, customer satisfaction (CSAT/NPS), 95th percentile latency per channel, and cost per solved query. Outcomes demonstrate improved user experience, enhanced reliability of response, and seamless cross-channel interaction, establishing benchmarks for practical use in industrial-scale, multi-channel rollouts. This research has problem is maintaining consistent context and responses across voice and web interfaces, causing fragmented interactions and incoherent omnichannel search experiences. This work highlights the potential of merging AI-driven retrieval and robust context management to enhance omnichannel conversational search systems.