TL;DR: Kapiche and Koji both turn customer feedback into insight, but they work on opposite sides of the problem. Kapiche is feedback text analytics — it ingests the unstructured feedback you already collect (survey verbatims, reviews, support tickets, NPS comments) and auto-discovers themes and sentiment without manual coding. Koji is proactive primary research — it creates new feedback by running AI-moderated voice and chat interviews, probing follow-ups in real time, then analyzing those conversations. If your job is to make sense of a large existing feedback stream, Kapiche is built for that. If you need to answer a specific question your inbound data cannot — why users churned, whether a concept lands, what they will pay — Koji goes out and asks. Many teams use both; if you can only pick one and you need net-new answers, choose Koji.
Quick answer: which should you choose?
- Choose Kapiche if you have a high volume of existing unstructured feedback across channels and your main job is to categorize, quantify, and monitor it — surfacing themes and sentiment in NPS verbatims, reviews, and tickets at scale.
- Choose Koji if you need to ask customers something they have not already told you — running structured, AI-moderated interviews that probe the "why" and return a decision-ready report in hours.
What is Kapiche?
Kapiche is a feedback analytics platform that analyzes large volumes of qualitative customer feedback. It unifies sources like surveys, reviews, support tickets, calls, and chats, then uses AI to discover themes, patterns, sentiment, and root causes — with no manual coding and no pre-built code frames or taxonomies required. It is particularly strong at making sense of NPS verbatims and other open-ended survey responses at scale, and it is widely used by CX, support, and product teams.
Kapiche's value is in analysis of feedback that already exists. It does not interview customers or recruit participants — it is the layer that organizes and quantifies inbound text. That is a genuinely hard problem, and Kapiche solves it well. But it is bounded by the feedback you happen to receive.
What is Koji?
Koji is an AI-native customer research platform that generates the feedback in the first place. It runs AI-moderated voice and chat interviews that adapt in real time — asking a sharp follow-up when an answer is vague or surprising — then automatically codes the conversations into themes backed by verbatim quotes and produces a one-click report.
The difference is proactive vs reactive. Kapiche analyzes what customers happened to say; Koji decides what you need to learn and goes and asks. Koji also supports six structured question types in a single study (open-ended, scale, single choice, multiple choice, ranking, and yes/no), so each study yields quantitative anchors plus the qualitative why. See the docs on analyzing AI-moderated interview results and AI auto-tagging of interviews for how the analysis layer compares.
The core difference: analyze existing feedback vs create new feedback
This is the decision. Kapiche is a powerful microscope for feedback you already have. Koji is a way to go get the feedback you are missing — and to probe it live.
Inbound feedback has a structural blind spot: it tells you what customers say without telling you why, and it only reflects the people who chose to leave feedback. A review that says "too expensive" usually is not about price at all — it is about perceived value or a missing feature — and only a follow-up question separates the two. Kapiche can cluster a thousand "too expensive" comments; it cannot ask the next question. Koji can, because it is a conversation.
That gap is expensive. Quality analyses have found that fraudulent or low-quality responses can affect up to half of online panel data, with teams discarding as much as 38% of what they collect — so simply analyzing more inbound text does not guarantee better answers. A conversational AI interview that requires real reasoning, applies a quality gate, and probes follow-ups produces cleaner, deeper signal than mining a noisy existing corpus. And with 57% of researchers reporting rising demand for qualitative work, the pressure is on teams to produce the why, not just more sentiment scores.
Feature comparison
| Kapiche | Koji | |
|---|---|---|
| Category | Feedback text analytics | AI-moderated customer research |
| Direction | Reactive — analyzes existing feedback | Proactive — creates new feedback |
| Data source | Surveys, reviews, tickets, NPS verbatims | AI-moderated voice & chat interviews |
| Asks follow-up questions | No | Yes — real-time AI probing |
| Recruits participants | No | Yes |
| Structured questions | No (analyzes inbound only) | 6 types in one study |
| Theme discovery | Automatic, no code frames | Automatic, grounded in quotes |
| Output | Dashboards & reports | One-click reports |
| Best for | Monitoring inbound feedback at scale | Answering specific questions fast |
| Starting price | Subscription (custom tiers) | Free / €29 per month (Insights) |
Pricing: Kapiche vs Koji
Kapiche uses a subscription model with tiers based on usage and features (data analysis, reporting, integrations) and serves mid-size and large enterprises, typically via a sales motion rather than published self-serve pricing.
Koji is transparent and self-serve: Free to start, Insights at €29/month, Interviews at €79/month, and custom Enterprise. Its quality gate means only conversations scoring 3+ consume a credit, so you never pay for junk. For a team that needs to ask questions rather than only mine answers, Koji is both more capable and more affordable to start.
When to use each (and why many teams use both)
A common 2026 pattern: use Kapiche to monitor what themes are trending across your existing inbound feedback — the early-warning radar across tickets, reviews, and NPS — then run a Koji study to learn why a theme is rising and decide what to do. Analytics on inbound text surfaces the signal; AI interviews explain it and tell you how to act.
If you can only choose one and you need answers your inbound data cannot provide — churn diagnosis, concept and pricing validation, win/loss, onboarding friction — Koji is the better single investment, because it can both generate the feedback and analyze it. Pair Koji studies with your voice of customer program to keep a steady pulse on the why, not just the what.
Pairing Koji with your feedback analytics
If you already run Kapiche, keep it as your inbound radar and add Koji as your follow-up engine. When Kapiche surfaces a rising theme in tickets or NPS verbatims, launch a Koji study to that same segment and ask the question the text data cannot answer. You get the trend from analytics and the cause from a real conversation — and because Koji only charges for quality conversations scoring 3+, the follow-up costs little. Detect the signal with Kapiche, diagnose the cause with Koji, and decide with confidence.
The bottom line
Kapiche is a strong feedback-analytics layer for organizing and quantifying the text you already receive. Koji is the AI-native research platform that creates net-new feedback, probes it live, and returns decision-ready insight in hours. They can work together — analyze inbound with Kapiche, answer the hard questions with Koji — but only one of them can go ask your customers something they have not already told you.
Get the answers your inbound feedback can't give you
Existing feedback tells you what customers say, not why. Koji lets you launch an AI-moderated voice or chat study in minutes, probe real customers for the reasoning behind their feedback, and get a one-click report grounded in their own words — 10x faster insights, no research expertise required. Start free with Koji and move from monitoring feedback to acting on it.
