Conversational Interviewer AI Agent
This AI agent leverages multi-page forms and conversational interview loops to collect qualitative feedback from customers or users. It automates question-and-answer sessions where the AI asks follow-up questions based on user responses, creating more dynamic interviews. It records all interactions, stores transcripts, and exports data for analysis. It reduces time and cost compared to manual interviews and allows scaling customer feedback collection.
Benefits
Dynamic Feedback Collection
Questions adapt automatically based on user answers, yielding deeper insights.
Scalable Interview Process
Handle many respondents at once without manual interviewer resources.
Data Capture & Export
Store transcripts and export to sheets or databases for rigorous analysis.
Reduced Research Costs & Time
Cuts down time and expenses compared to traditional customer interview processes.
How It Works
Session Initiation
A multi-page form trigger starts the interaction and creates a session to track responses.
Looping Interview
AI agent asks questions in loop form, continuing until user decides to end interview.
Contextual Recording
Each answer is recorded in session storage (using a Redis-compatible backend) for context.
Data Export
On finishing, the system exports the data (e.g. to sheets) for team sharing and analysis.
Customization Options
Customisation is possible by swapping the LLM, adjusting form structure, or adding knowledge retrieval tools.
Use Cases
Customer Feedback: Retail brands collecting customer satisfaction feedback after purchase.
Market Research: Market research to understand preferences for new store-design or product features.
Client Onboarding: Onboarding new clients by interviewing them about their needs and expectations.
Campaign Follow-up: Follow-up interviews post-promotion or campaign to measure impact.
Integration and Customization
Form/UI Framework
Multi-page forms for structured user responses and branching logic.
Session Storage (Redis or equivalent)
Maintains transcript and context across the interview loop.
Export Destination (e.g. Sheets, Database)
Allows storing completed interviews for team review and data analysis.
AI Language Model Pluggable Backend
Swap the underlying LLM / model provider; add knowledge base retrieval if needed.