1
Research· 2 weeks
Interview methodology mapping
Studied qualitative research methods and interviewed 8 user researchers to understand what makes a good interview - question flow, probing techniques, rapport building.
Back to products
SaaS
AI Interview Agent
Voice-powered customer research on autopilot
Customer surveys get 5-10% response rates and surface-level answers. Phone interviews get depth - but cost $50-100 per interview to conduct manually. AI Interview Agent conducts structured voice conversations at scale, with AI-powered analysis that surfaces patterns humans miss.
Live
6x
Deeper insights than surveys
90%
Completion rate
$5
Per interview (vs $100 manual)
The problem
Companies need customer insights to make good product decisions, but the methods are broken. Surveys get low response rates and shallow answers. Manual interviews are expensive and don't scale. Focus groups are slow and biased by group dynamics. Most companies end up making decisions on gut feel because real qualitative data is too expensive to gather.
Cost of inaction
A single round of 50 customer interviews costs $5,000-$10,000 with a research agency and takes 4-6 weeks. Most companies can't afford to do this more than once a year - so they make product decisions blindly for the other 11 months.
Our approach
We studied how experienced user researchers conduct interviews - the question flow, the probe techniques, the way they follow interesting threads. The best interviews feel like conversations, not interrogations.
The AI agent uses a script as a guide, not a script to read. It asks the planned questions but follows up naturally when it detects interesting signals - surprise, frustration, enthusiasm. This is where the real insights live.
Synthesis was the harder problem to solve. Raw transcripts are useless at scale. We built an analysis layer that identifies themes, sentiment patterns, and outlier responses across hundreds of interviews - delivering the report a research team would take weeks to produce.
Key insight
Voice conversations capture nuance that surveys miss - tone, hesitation, enthusiasm, off-script tangents. AI can now conduct these conversations naturally AND analyze the qualitative data at scale. The combination of human-quality conversation with machine-scale analysis changes what's economically possible.
What AI Interview Agent does
Multi-language voice interviews
AI-powered sentiment analysis
Automated transcript summaries
Real-time analytics dashboard
CRM integrations
Customizable interview scripts
Follow-up question intelligence
Batch outbound calling
Results
95% lower
Cost per interview
90%
Completion rate
24 hours
Time to insights
AI-conducted interviews cost $3-8 each compared to $50-100 for manual interviews with a research agency.
Phone conversations have dramatically higher completion rates than email surveys (5-10%) because the interaction is immediate and personal.
From launching a batch of 200 interviews to receiving a synthesized report with themes, sentiment analysis, and recommendations.
Architecture
Tech stack
PythonFastAPITwilioOpenAI WhisperGPT-4PostgreSQLRedisAWS
Key decisions
Conversational AI over rigid scripts
Rigid question-answer flows miss the gold - the unexpected insights that surface when an interviewer follows an interesting thread. The AI adapts in real-time based on response analysis.
Real-time transcription + post-call synthesis
Whisper transcribes during the call for follow-up question generation. Full synthesis happens post-call when the entire conversation context is available.
Batch orchestration for scale
Research projects need 50-500 interviews. We built a batch calling system with scheduling, retry logic, timezone awareness, and progressive pacing to maximize completion rates.
How we built it
2
Voice· 5 weeks
Conversational AI pipeline
Built the speech-to-text, LLM reasoning, and text-to-speech pipeline with Twilio for telephony. Achieved natural conversation cadence with <500ms response latency.
3
Intelligence· 3 weeks
Analysis & synthesis engine
Built the cross-interview analysis layer - theme extraction, sentiment tracking, outlier detection, and automated report generation.
4
Integration· 2 weeks
CRM & workflow connections
Added Salesforce, HubSpot, and Slack integrations so insights flow into existing workflows without manual export.
What we learned
Latency is the enemy of natural conversation
If the AI takes more than 500ms to respond, the conversation feels robotic. We optimized the entire pipeline - streaming transcription, cached reasoning, and pre-generated response fragments - to keep it under 400ms.
The synthesis is more valuable than the interviews
Clients rarely read individual transcripts. The cross-interview analysis - themes, patterns, outliers - is what drives decisions. We invested heavily in the report quality.
People are surprisingly honest with AI interviewers
We expected lower candor with AI. The opposite happened - respondents were more honest because they felt less social pressure. No interviewer bias, no judgment.
Growth strategy
Targeted product teams and UX researchers at mid-market SaaS companies who already run customer interviews but can't scale them. The cost comparison ($5 vs $100 per interview) is compelling enough to drive trial.
Frequently asked questions
Very natural. We use advanced text-to-speech with sub-400ms response times, natural pauses, and conversational fillers. Most respondents don't realize they're talking to AI until told.
Next Step
Need AI-powered voice interactions?
We built the voice AI pipeline, conversational intelligence, and batch orchestration. Tell us your use case - customer research, sales qualification, support - and we'll scope it.