OpenInterviewer: AI-Powered Qualitative Interviews

OpenInterviewer is an open-source platform for conducting qualitative research interviews at scale. Instead of scheduling dozens of hour-long interviews, you design your study and share a link — participants engage with an AI interviewer that adapts based on their responses.

When to Use OpenInterviewer

• Pilot studies — Test interview protocols before committing to full human-conducted studies
• Scale — Collect interview data from 50+ participants without scheduling constraints
• Exploratory research — Rapidly gather perspectives on emerging topics
• Cross-timezone studies — Participants engage on their own schedule
• Complementary data — Pair with human interviews for methodological triangulation

How It Works

For Researchers

1. Create a study — Define research questions, participant profiles, and interview mode
2. Configure the interviewer — Choose structured, standard, or exploratory mode; select your AI model
3. Share the link — Participants access via a simple URL (with optional expiration)
4. Monitor and analyze — Real-time synthesis of themes, contradictions, and patterns
5. Generate follow-ups — Create new studies based on synthesis findings to dig deeper

For Participants

1. Open the link — No account or app required
2. Consent — Standard consent flow
3. Conversation — Natural dialogue with an AI interviewer that adapts to responses
4. Demographics — Collected conversationally, not as a separate form

Key Features

Interview Modes

Model Selection

Each study can use a different AI model, selected from a dropdown in the study setup page. This lets you balance cost, speed, and quality per study.

Model priority: per-study UI selection takes precedence over environment variable defaults.

AI Reasoning Mode

For analytical operations like synthesis, the platform can auto-upgrade to premium models with extended thinking enabled — producing richer thematic analysis without slowing down the interview itself.

Each study can override this behavior: Automatic (recommended default), Always enabled (slower interviews but deeper responses), or Always disabled (faster, uses your selected model throughout). Keep in mind that auto-upgraded synthesis uses premium models — monitor costs if running many interviews.

Built-in Analysis

• Per-interview synthesis — Automatic extraction of stated vs revealed preferences, themes, and contradictions
• Cross-interview analysis — Pattern identification across all participants
• Aggregate reporting — Themes, outliers, and convergence points
• Follow-up studies — Generate new research questions from synthesis findings to iteratively deepen your inquiry

Link Management

When generating participant links, you can set expiration windows (7 days, 30 days, 90 days, or never) and toggle link access on or off from the study detail page. This is useful for closing data collection on a schedule, pausing a study, or revoking links if they've been shared beyond your intended sample.

Security

• API keys stay server-side, never exposed to participants
• Researcher dashboard is password-protected
• Participant tokens are JWT-signed
• Data stored in encrypted Vercel KV (Redis)

Architecture

Built on Next.js with a clean separation between researcher and participant flows:

Researcher Dashboard (password-protected)
  ├── Study creation and management
  ├── Per-study model and reasoning configuration
  ├── Real-time monitoring
  ├── Cross-interview analysis
  └── Follow-up study generation

Participant Interface (public link)
  ├── Consent flow
  ├── AI-conducted interview
  └── Demographic collection

Backend
  ├── AI provider abstraction (Gemini / Claude)
  ├── Vercel KV for data persistence
  └── JWT-based participant authentication

Deployment

The fastest path is one-click Vercel deployment — click the button below, set two environment variables, and you're live in about two minutes.

For local development:

git clone https://github.com/linxule/openinterviewer.git
cd openinterviewer

bun install
cp .env.example .env.local
# Edit .env.local with your API keys
bun run dev

Environment Variables

Vercel KV credentials (KV_REST_API_URL, KV_REST_API_TOKEN, etc.) are configured automatically when you connect an Upstash Redis store through the Vercel dashboard.

Methodological Considerations