AI Voice Interviewer Platform
Founding engineering on a full-stack platform for launching large-scale AI-conducted consumer research studies. Built the end-to-end flow from study configuration to AI voice agent deployment to automated PPT and PDF reports.
Client
Consumer research startup
Industry
AI Survey & Consumer Research
Services
Founding Engineering, AI Voice Integration, Full-Stack Development, Automated Reporting, LLM Pipelines
Date
Jan 2026
The Challenge
Traditional consumer research is slow and expensive. Recruiting participants, scheduling 1:1 interviews with skilled moderators, transcribing recordings, and synthesizing findings into a deck can take weeks for a single study — and the cost per interview is prohibitive at scale.
The bet behind this product: what if an AI voice agent could conduct hundreds of natural, open-ended consumer interviews in parallel — and turn the results into a research-grade report automatically?
What We Built
As founding engineer on this platform, we shipped the full-stack application that makes large-scale AI voice research practical.
Study Configuration
A web interface that lets a researcher define a study in minutes: target audience, screening criteria, interview script, follow-up logic, and success metrics. Designed so non-engineers can launch sophisticated multi-branch interview flows without writing a line of code.
AI Voice Agent Deployment at Scale
Integrated a production voice AI provider to deploy AI voice interviewers that conduct phone or web-based conversations with real participants. The agents handle natural back-and-forth, ask intelligent follow-ups based on what the participant just said, and stay on script when needed.
The system orchestrates many concurrent voice sessions, monitors call quality and completion, and gracefully handles the messy reality of phone interviews (drop-offs, recording failures, retries).
Automated Analysis & Reporting
Once interviews are complete, an LLM pipeline (LangChain + Python) processes the raw transcripts into structured insights:
- Theme extraction across hundreds of conversations
- Sentiment and language pattern analysis
- Direct quote pull-outs that support each finding
- Statistical breakdowns by participant segment
The reporting layer generates client-ready PPT and PDF reports automatically — the kind of deliverable that used to take a research analyst days to assemble manually.
Tech Stack
| Layer | Technologies |
|---|---|
| Frontend | TypeScript, React |
| Backend | Node.js, TypeScript |
| AI Pipeline | Python, LangChain |
| Voice | Voice AI provider |
| LLMs | OpenAI |
| Reporting | Automated PPT and PDF generation |
Why This Matters
Voice AI is moving from "novelty demo" to "production research tool" remarkably fast. The hard parts aren't the voice agent itself — they're the orchestration around it: study setup, participant flow, recovery from real-world failure modes, and turning thousands of minutes of audio into something a brand team can actually act on.
That orchestration layer is exactly the kind of work small businesses will need when they want to deploy AI agents into their own operations.