Chat With Your Interview Transcripts: How Koji Lets You Query 100 Customer Interviews at Once

The Bottom Line

Chatting with interview transcripts is exactly what it sounds like: instead of opening a doc, scrolling through 12,000 lines of conversation, and trying to remember which participant said what, you type a plain-English question — "Which participants pushed back on the proposed pricing?" — and the AI returns a direct answer plus the verbatim quotes that prove it. Koji's Insights Chat does this across your entire research corpus, not one transcript at a time. You can query a single study, the last quarter of interviews, or every customer conversation you've ever run through the platform.

The reason this matters: by year three of a continuous discovery practice, most teams have hundreds of transcripts. Manual re-reading is dead. Without a chat layer, all that research becomes write-only memory — captured, never re-read, gradually forgotten. With a chat layer, every past interview is queryable evidence the next time a PM asks "have we ever asked customers about X?"

This guide covers what transcript chat actually does, the four query patterns that get the most value out of it, how Koji prevents fabricated quotes, and the workflows that turn a stale research repository into a living institutional memory.

What "Chat With Transcripts" Actually Means

The interface is a chat box. You type a question. The AI responds with:

1. A direct answer in plain prose — "Six participants raised concerns about pricing. Four said it felt high for their team size; two cited specific competitor anchors."
2. Verbatim quotes with participant identifiers and timestamps — so you can verify the answer is grounded in real data.
3. A frequency count — "6 of 15 participants" — not a vague "many users."
4. Links back to the source transcripts — one click and you're reading the original conversation with the highlighted span in context.

What distinguishes a real transcript-chat tool from a glorified ChatGPT wrapper is whether (a) it grounds every answer in the actual transcripts and (b) it scopes the search correctly. Koji's Insights Chat does both: it uses retrieval over the indexed transcripts, then constrains the LLM's answer to only the retrieved spans. If the answer can't be supported by a real quote, it says so — instead of inventing.

The Four Query Patterns That Get the Most Value

After watching hundreds of teams use Insights Chat, four query patterns generate 80% of the insight value. Learn these and you'll get answers that move decisions.

1. The disconfirming evidence query

"Did anyone push back on the pricing model?"

"Which participants disliked the new onboarding flow?"

"Find quotes that contradict our hypothesis that users want a dark mode."

This is the highest-leverage pattern because most research reports over-index on confirmation. The default summary tells you what people agreed with; the disconfirming query forces the AI to surface the dissent. When you ask it explicitly, the chat returns the 2-of-15 minority voice that the synthesis might have flattened.

This is where the chat layer earns its keep. Without it, finding three skeptical quotes in 40 hours of transcripts takes a full afternoon. With it, you have them in 15 seconds.

2. The cross-study pattern query

"Across all churn studies in the last 18 months, what's the most common reason for downgrading?"

"Compare what enterprise vs SMB users said about integrations."

"Have any participants ever mentioned wanting a Slack integration?"

Cross-study queries are impossible in a transcript-by-transcript world — you'd need to open 12 reports and synthesize manually. With chat, the AI runs the retrieval across every study you have access to and surfaces patterns across boundaries. This is what turns a research repository into a real corporate memory: any team can ask a question and get evidence from work other teams have already done.

3. The persona-specific query

"What did design leaders (vs ICs) say about the new layout?"

"Which trial-tier users mentioned switching from Notion?"

"Show me responses from participants in the EU."

Koji's chat layer respects participant metadata. If you've tagged participants by role, industry, plan tier, or geography (see research screener questions), the chat can filter answers by that segmentation. This is especially powerful for B2B research where the same product can land very differently for an SDR vs a VP of Sales.

4. The "have we already studied this?" query

"Have we run any research on AI summarization features?"

"What do we know about onboarding for power users?"

"Find every study where we asked about pricing."

This is the most underrated pattern. It's how research stops being write-only. Before kicking off a new study, ask the chat what's already known. Half the time you'll discover a prior study answered the question and you can skip the study entirely. The other half, you'll start the new study with grounded priors instead of from scratch.

Most research teams lose this institutional knowledge because their old transcripts live in Google Drive folders nobody reads. Koji's repository view (combined with chat) makes it queryable on demand.

How Koji Prevents Fabricated Quotes

The biggest risk with any LLM-based transcript chat is fabrication — the model "remembering" a quote that doesn't exist. Koji prevents this with three architectural choices:

1. Retrieval-grounded responses

Every answer is generated after the retrieval layer has fetched the actual transcript spans. The LLM doesn't recall from its training data — it reads the retrieved chunks and synthesizes. If retrieval returns nothing, the chat says "no results found" instead of inventing.

2. Quote verification before display

Every quote returned in a chat answer is re-verified against the source transcript before it appears in the UI. If the LLM paraphrased instead of citing verbatim, the verifier catches it and either fixes the citation or marks the claim as unsupported. This is the same verification pattern described in how AI interviewers work — applied to chat outputs.

3. Participant ID requirements

Every quote carries a participant identifier and timestamp. If the chat can't resolve a quote to a real participant in your corpus, it doesn't show the quote. This eliminates the "as one customer said…" hallucination that plagues general-purpose chatbots applied to research.

For a deeper look at how Koji handles AI fidelity across all layers, see AI interview hallucinations & bias mitigation.

Setting Up a High-Signal Transcript Chat Corpus

A chat layer is only as good as the underlying transcripts. Three setup choices determine whether your chat returns gold or noise:

Tag participants on the way in

When you publish a study in Koji, the screener can capture any metadata — role, industry, plan tier, account ID, persona, region. Add the fields you'll want to filter by before the interview, not after. A transcript without participant context is searchable but not segmentable, and segmentation is half the value of chat. See research screener questions for the standard screener fields.

Use structured questions for quantitative anchors

Qualitative chat is powerful, but it's much more powerful when paired with structured questions (scale, single_choice, multiple_choice, ranking, yes_no — see structured questions guide). When the chat says "6 of 15 participants rated onboarding 2 or below on a 5-point scale," the number comes from typed data, not a vague qualitative impression. The hybrid — open-ended narrative + structured quantitative anchor — is what makes Koji's chat output defensible.

Run the quality gate

Low-quality interviews pollute the chat. A participant who gave one-word answers across 12 questions adds noise without signal. Koji's quality gate automatically scores each interview 1–5 on the composite quality metric and excludes low-quality transcripts (1 or 2) from the indexed corpus by default. This means your chat only retrieves from defensibly-rich conversations.

Sample Chat Session

Here's a representative session from a real Koji user — a product team kicking off a Q3 strategy planning week:

You: What's our strongest evidence that users would pay for a team plan?

Koji: 9 of 23 participants across the two pricing studies (Mar 2026 and Aug 2026) explicitly expressed willingness to pay for a team plan. The strongest signal came from agencies and small consultancies — 6 of 9 affirming quotes came from these segments.

Verbatim:

• "I'd pay $50 per seat tomorrow if it had role-based permissions." — P-3024, Agency Owner
• "We're ready to upgrade to a team plan as soon as you have one." — P-3091, Consulting Lead
• "Team pricing is the only thing keeping us on the individual plan." — P-2887, Small Studio

You: Did anyone say they wouldn't pay?

Koji: 4 of 23 participants pushed back on a team plan. Three cited their team being too small to justify per-seat pricing; one wanted unlimited seats at a flat rate.

• "Our team's only three people — per-seat would actually cost us more than the individual plan." — P-2912, Founder
• "Why per-seat? Just charge me a flat team rate." — P-3107, Director of Operations

Notice the pattern: every claim has a count, every quote has a participant ID, and the disconfirming evidence is immediately available with a follow-up question.

Where Transcript Chat Replaces Old Workflows

Teams using Koji's Insights Chat regularly retire these older workflows:

• The "let me re-read the transcript" meeting. Replaced by a real-time chat session in the meeting itself.
• The "I'll go look at the spreadsheet" follow-up. Replaced by an answer with cited quotes inside the chat.
• The "did we ever ask about X?" Slack thread. Replaced by a single chat query.
• The custom Looker dashboard built off transcript tags. Replaced by direct natural-language queries over the underlying data.
• The "what would users say if we asked?" hypothetical. Replaced by "what did users actually say when we asked?"

This is what it means to operationalize a research repository — making the data return value every week instead of once at publication.

When to Use Chat vs. a Full Report

Chat is the right tool when:

• You have a specific question and need an answer in under a minute.
• You're looking for disconfirming evidence in a published study.
• You're running cross-study queries.
• You're checking whether something has already been studied.

A full research report is the right tool when:

• You're publishing a study with formal stakeholders.
• You need a single canonical synthesis to circulate.
• You're archiving findings for compliance or knowledge management.
• You need themes ranked, charts rendered, and recommendations formatted.

The two work together. The report is the official record; the chat is the everyday tool that turns the report from a PDF into a living conversation.

How to Start

1. Sign up at koji.so — free tier, no credit card.
2. Publish at least one study so you have transcripts indexed.
3. Open the Insights Chat panel from the study or repository view.
4. Start with a disconfirming evidence query — it's the highest-leverage pattern for new users.
5. Click any cited quote to jump directly to the source transcript with the span highlighted in context.

The whole workflow is in insights chat guide and the underlying data structure is documented in understanding themes and patterns.

Related Resources

• Insights Chat Guide — Full workflow for natural-language transcript queries in Koji
• Structured Questions Guide — How the 6 question types ground qualitative chat answers in real frequency counts
• AI Interview Hallucinations & Bias Mitigation — How Koji prevents fabricated quotes in chat responses
• Understanding Themes and Patterns — The data structure that powers cross-study queries
• Reading Your Research Report — When to use chat vs. a formal report
• How the Quality Gate Works — Why low-quality transcripts are excluded from the chat corpus