When the AI Starts the Conversation (www.withcoherence.com)

Coherence flips the familiar prompt-response model by having an AI agent initiate and drive interviews instead of passively waiting for users to fill out forms. The agent asks follow-ups, digs into surprising answers, and adapts question paths in real time—mimicking a human researcher to surface richer qualitative insights. That approach targets chronic problems like low survey completion and shallow multiple-choice data, with clear use cases across product research, growth, HR audits, and content gathering where conversational nuance and context reveal why users churn, drop off, or feel a certain way. For the AI/ML community this matters because it turns passive telemetry into active, targeted data collection that requires robust dialogue management, context/state tracking, NLU, and safety controls. Implementations will likely combine few-shot prompts, intent recognition, dynamic dialogue policies, and human-in-the-loop or RLHF tuning to balance curiosity with brevity. Key challenges are design and adoption—making conversations feel natural, respecting user time and privacy, avoiding sampling and interviewer biases, and instrumenting evaluation metrics for response quality. If done right, agent-driven surveys can produce higher-quality labeled data and more actionable insights, but they demand careful UX, privacy safeguards, and analytics pipelines to translate richer conversations into reliable product decisions.