Reports & Analysis
20 articles
Understand how Koji analyzes interviews and generates research reports, themes, and actionable insights.
Asking Questions of Your Interview Transcripts with Koji and MCP
Koji has no chat box over transcripts. The report answers first and links every claim to the quote behind it, and the Koji MCP connector lets Claude, Cursor or any MCP assistant read your study data and transcripts and answer free-form questions, including across studies.
Can You Trust AI Interviewers? How Koji Prevents Hallucinations and Bias in Customer Research
A practical guide to how modern AI research platforms prevent hallucinations, model bias, and leading questions during auto-moderated customer interviews — with the verification techniques Koji uses to keep AI-generated insights faithful to the actual transcript.
How to Read Your Koji Research Report: A Section-by-Section Guide
A complete walkthrough of every section in a Koji research report — from the overview and themes to quantitative charts, key quotes, and the quote behind every claim, so you can extract maximum value from your findings.
Exporting Research Data from Koji: CSV, JSON, and Transcript Access
A complete guide to every way you can get your interview data out of Koji — from one-click CSV downloads to automated API pipelines.
AI Interviewer Tuning: How to Get Research-Grade Voice Interviews
A complete playbook for tuning Koji's AI interviewer — company context, probing depth, structured questions, and interview mode — to deliver interviews indistinguishable from a human researcher.
Viewing Interview Transcripts
How to read, navigate, and get value from your interview transcripts in Koji.
AI Research Agent: How Autonomous AI Conducts User Interviews End-to-End
A practical guide to AI research agents — autonomous systems that design studies, run conversational interviews, and synthesize findings without a human moderator. Includes how Koji's agentic stack works under the hood.
Conversation Intelligence for Customer Research: A Practical Guide
Conversation intelligence is moving from sales calls into customer research. This guide explains what it means for product, UX, and market research teams — and how an AI-native research platform applies it across every customer conversation.
Understanding Quality Scores
Learn how Koji evaluates interview quality on a 0-5 scale and why it matters for your research and billing.
AI-Generated Insights
Discover what analysis Koji automatically produces for each interview — themes, sentiment, key quotes, and findings.
Asking Questions About Your Research Data in Koji
Koji has no chat box over your study data. Here is what answers your questions instead: every interview analysed on its own, a report whose every claim links to the quote behind it, personas and question coverage, the responses grid, CSV and JSON export, and the MCP connector for free-form questions.
Generating Research Reports
Create comprehensive aggregate reports across all your interviews — including summaries, themes, recommendations, and statistics.
Understanding Themes & Patterns
Learn how Koji identifies recurring themes across interviews and how to use them for decision-making.
Publishing & Sharing Reports
Make your research reports accessible to stakeholders, team members, and decision-makers.
Insights Dashboard
Navigate visual analytics including interview counts, completion rates, quality distributions, and participant statistics.
Real-Time Research Insights: How to See Themes, Quotes, and Quality Scores as Interviews Complete
Stop waiting weeks for analysis — modern AI research platforms surface themes, structured-question distributions, sentiment, and quality-scored quotes the moment each interview ends. Here is how real-time research insights work in Koji and how to design studies that take advantage of them.
Adaptive AI Interviews: Branching Logic That Personalizes Every Question
How AI-moderated interviews replace static survey branching with dynamic, conversational follow-ups. A practical guide to designing adaptive interview flows with Koji.
AI Auto-Tagging for Customer Interviews: Code 100 Interviews in Minutes
How AI auto-tagging compresses 40+ hours of manual qualitative coding into minutes. Covers the two-cycle coding approach Koji uses (descriptive cycle-1 + axial cycle-2), the difference between auto-tagging and thematic analysis, building a codebook the AI respects, and how to validate AI-generated tags against your standards.
Answer Confidence in Koji Reports: What High, Medium and Low Actually Mean
Every structured answer in a Koji report carries a high, medium or low confidence flag describing how certain the analysis is that it mapped the right transcript span to the right question. Here is what each level means and what to do about it.
When Participants Go Off-Script: How AI Interviews Stay On-Brief
Participants wander off topic and occasionally test the interviewer with instructions of their own. How Koji separates useful drift from derailment using scope bounds, interview modes and quality scoring.