Koji vs Talkful

Koji vs Talkful is a comparison between two AI-native research tools that almost rhyme on the surface, and pick different things to optimize once you scratch the paint. Koji is an Amsterdam-built customer research platform with AI-moderated async voice and text interviews, six structured question types, EU data residency, and a credit-based price model that meters voice conversations at three credits each. Talkful is AI-powered async user research for product teams. Researchers share a link, and participants answer in voice, text, choice, or rating. An AI interviewer asks smart follow-ups in real time at a depth the researcher picks, and a synthesis engine streams themes, quotes, and citations back as the responses land, ready for the team to ship from or for the agents you build with to act on.

Both teams can sign up with a credit card. Both ship a free tier. Both produce structured output a downstream agent can act on. After that, the products diverge on pricing shape, modality model, and how the synthesis loop runs.

Where Koji wins

Koji has been heads-down on AI-native customer research since 2024, when Nirmay Panchal and Luca Lago founded the company in Amsterdam and started shipping against the same async interview pattern Talkful is also building in. Five places the product is genuinely strong:

• EU data residency and GDPR posture, built in from day one. Koji's homepage and about page make EU data residency a first-class promise: the platform runs on European infrastructure, with a GDPR-aligned data model, and a small Amsterdam team that lives inside the regulatory frame their European customers operate under. For a German PM, a Dutch CX team, or a French insights org that has to defend the vendor to a DPO before the contract closes, that posture is a real procurement advantage. Talkful is built on Supabase, Cloudflare R2, and Vercel today: a different infrastructure shape, and a different conversation with a European DPO.
• Six structured question types in one study. Koji's question catalog (open-ended, scale, single choice, multiple choice, ranking, yes / no) is more granular than Talkful's four modalities (voice, text, choice, rating). For a PM who wants to ladder a NPS-style rating into an open follow-up, then ask a ranking question on feature priorities, then a yes / no qualifier, that catalog maps cleanly onto a 5-to-10 question study. Talkful covers the high-leverage modes (voice, text, choice, rating) and leans on the rating modality plus smart follow-ups for the same work; the abstractions are different, and Koji's catalog is the closer match for survey-shaped studies.
• A research-first agent that adapts inside the conversation. Koji's positioning is built around "AI-moderated voice and text interviews" with adaptive follow-ups inside the same conversational turn. The agent decides whether to probe further, the same way a thoughtful moderator would, and the team's reported claim is up to three follow-ups per topic. For a researcher who wants the AI to handle the conversation shape on their behalf, that single-agent flow is the buying argument the product is built around. Talkful expresses the same problem differently (see "Where Talkful wins" below), and the trade-offs are genuine.
• One-click thematic reports with quote traceability. After the study runs, Koji compresses the full transcript set into a study-wide report: themes, recommendations, and verbatim quotes traceable back to the exact participant. The team's own framing is "roughly 28x faster than manual coding". For an agency or in-house researcher whose downstream artifact is a report a stakeholder will actually read on a Tuesday morning, the report-first output shape is a real time-saver. Talkful's synthesis output is a live insights dashboard with citation-grade quotes and 15-second audio anchors, which is a different artifact shape on a different cadence (see below).
• Backed by named startup programs. Koji's homepage lists Google for Startups and ElevenLabs Grants among its backers. That is not a Series A, but for a 2024-founded team it is meaningful credibility on infrastructure access and signals serious traction inside the AI-native research category. Talkful is younger as a brand on the market and has not raised institutional rounds; for a buyer who weights backer signals at the procurement stage, that is a Koji edge today.

If the work is "run 20 to 50 AI-moderated voice or text discovery interviews per week on a European customer list, with EU data residency, a structured question catalog, and a one-click report at the end", Koji is solving exactly that problem with the right buyer posture.

Where Talkful wins

Talkful is building in a slightly different lane on purpose. Five places where AI-powered async user research with real-time synthesis wins outright:

• Smart follow-ups expressed as configurable depth, not a fixed agent style. After a participant submits a voice, text, or rating answer, a fast LLM decides whether one or more clarifying questions would sharpen the response, then shows each as a separate full-screen step the participant can answer in their preferred mode or skip. The researcher picks the depth per question: shallow (at most one probe, for low-friction in-product feedback where dropoff matters), medium (a small chain when the answer is vague or contradicts itself), or expert (the AI keeps probing until it has the same context a senior researcher would dig out in a moderated interview: contradiction, scope, who, when, prior alternatives tried). The participant retains the right to skip on every probe. Koji's agent runs adaptive follow-ups, with a soft cap around three per topic; Talkful exposes the depth choice as a methodology setting the PM picks per question. Different shape, same problem (chase the "why" beyond the first answer), opposite trade-off (single-style conversational agent vs researcher-controlled depth). Our piece on AI follow-up questions in user research goes deeper on why the depth choice belongs with the researcher.

• Synthesis that streams while the study is still collecting. Themes, mention counts, sentiment, and citation-grade quotes form as responses land, with 15-second audio clips attached to each insight card so a stakeholder can hear the exact moment that backs the theme. A product team can act on signal mid-study, share a live insights link with engineering or design, and pipe structured output (themes, quotes, audio anchors) into the tools the team and the agents they build with already use. Koji compresses the synthesis into a one-click report after the study completes; Talkful runs the synthesis loop on each response at collection time, with an aggregate Claude Sonnet pass once the participant target is hit. Our guide to synthesizing user research covers the live-loop pattern in detail.
• A flat workspace fee, not a credit-metered conversation count. Talkful Starter is $29/mo (annual) for 100 participants per month. Pro is $79/mo (annual) for 1,000 participants per month. Free is $0 for 10 participants per month. Every plan, including Free, comes with unlimited studies and unlimited workspace users, and the full AI synthesis pipeline. See the pricing page for the full table. Koji's Insights plan is €29/mo for 29 credits, and Interviews is €79/mo for 79 credits, with each voice interview costing three credits (≈ 9 to 26 voice interviews per month before flex add-ons). For a four-person team running three voice studies a month with 30 to 50 participants each, Talkful Starter at $29/mo covers the work flat; Koji's matching Interviews plan covers ~26 voice interviews before more credits are needed. Past 100 voice answers a month, the curves cross hard. Below 20 voice interviews a month with the rest as text chats, Koji's credit math can actually work out cheaper.
• One link, designed to live anywhere, including churn flows and internal stakeholder reviews. A Talkful study link is a standing instrument for collecting signal, not a session campaign with a start and end date. The same link works in a product help menu, on a cancel-confirmation page, in a post-onboarding email, on a marketing landing page, in a Slack community, and in an internal stakeholder review (engineering, design, support, or legal weighing in on a prototype before it ships). Every response routes through the same synthesis pipeline regardless of where it came from. Koji's primary placement is a shared link or email distribution against a recruited list of interview participants, with the agent expected to "run an interview" rather than passively collect signal across surfaces. Our guide to building a customer feedback loop covers where standing-link placements actually pay off, and continuous discovery interviews covers the cadence we recommend.
• Four-modality answers, including in-line choice and rating without a separate survey product. Talkful's question types let the participant answer in voice, text, choice, or rating from inside the same study, with smart follow-ups triggered on voice, text, and rating answers. For a product team that wants a 1-to-5 satisfaction rating plus a follow-up "what felt off" voice answer, then a multiple-choice qualifier later, the modalities sit on the same canvas. Koji's six-question catalog is closer to a structured survey shape: rich for survey-style studies, but the framing is "the AI moderates a structured conversation" rather than "the participant picks the answer mode per question". The interaction patterns are different on purpose; for many product teams, the latter is the right shape for weekly research. For voice answers specifically, the pattern is the same one billions of people already use to send voice messages on WhatsApp: a familiar shape for the participant, with no camera and no scheduled call.

If the research question is "what are my users actually trying to tell me about this product decision, by Friday", Talkful is built for that question with synthesis that streams in real time. Our guide to running AI-moderated user interviews covers when async multi-modal answers with researcher-picked depth are the right collection medium.

Pricing, side by side

Koji pricing (public at koji.so/pricing, verified June 2026):

• Free: 10 credits on signup, no card required. Enough for a real interview before any spend, and the "only valid conversations billed" quality gate means short or broken sessions do not draw down the balance.
• Insights: €29/mo. 29 credits per month, AI-moderated voice and text interviews, adaptive probing, the six structured question types, one-click insight reports.
• Interviews: €79/mo (or €790/yr). 79 credits per month, voice interviews, advanced analytics. Each voice interview is 3 credits (≈ 26 voice interviews per month at this tier); each text chat is 1 credit; report refreshes are 5 credits.
• Flex / Enterprise: pay-as-you-go top-up credits and custom Enterprise contracts available on request for higher volumes and EU data-residency commitments at scale.

Talkful pricing (public at talkful.io/pricing):

• Free: $0. Up to 10 participants per month. Unlimited studies and unlimited users. Full AI synthesis pipeline. "Powered by Talkful" footer on participant pages.
• Starter: $29/mo (annual) or $39/mo (monthly). 100 participants per month, unlimited studies and users, ask AI anything about your study, CSV / JSON export, full AI analysis, email support.
• Pro: $79/mo (annual) or $99/mo (monthly). 1,000 participants per month shared across the workspace, unlimited studies and users, Slack integration, priority email support, no branding.

The shape of the unit is different. Koji meters by credits, with voice interviews drawing three credits each, so a €79/mo Interviews plan covers ~26 voice interviews before flex top-ups kick in. Talkful meters by completed participant sessions on a study link, so a $79/mo Pro plan covers up to 1,000 participants per month on the workspace at the same dollar order of magnitude. For a team running 5 to 25 voice interviews a month against a tightly recruited list, Koji's credit model has real upside (per-conversation cost is transparent, and the quality gate refunds broken sessions). For a team running 50 to 1,000 mixed-modality participants a month through a standing link, the flat workspace fee is the right shape and the curves cross past ~30 voice interviews a month. Currency matters at the margin too: Koji prices in euros, Talkful prices in US dollars, and the FX moves the comparison by a few percent on any given day.

Koji vs Talkful: which should you pick?

Neither tool is wrong for its audience. The buyer sorts the decision.

Choose Koji if:

• EU data residency and GDPR posture are non-negotiable procurement requirements
• Your study shape leans on survey-style structured questions (scale, ranking, single / multiple choice, yes / no) alongside open-ended voice or text
• You want a single AI moderator running the full conversation, not a researcher-controlled depth choice per question
• A one-click thematic report at the end of the study is the right output artifact for the cadence you ship on
• A credit-metered model fits the work better than a flat workspace fee at your interview volume (typically 5 to 25 voice interviews per month)
• You weight backer signals (Google for Startups, ElevenLabs Grants) at the vendor-selection stage

Choose Talkful if:

• Your research question is "what are my users actually trying to tell me about this product decision, by Friday"
• You want smart follow-ups expressed as a methodology setting (shallow, medium, or expert) per question, with the participant always free to skip
• You want themes, quotes, sentiment, and 15-second audio clips forming on the dashboard while the study is still collecting, with structured output your agents can act on
• You want a single shareable link you can place in a product help menu, on a cancel-confirmation page, in a post-onboarding email, in a Slack community, or in an internal stakeholder review before shipping a prototype, with every response routed through the same synthesis pipeline
• You want a flat workspace fee that does not climb with credit consumption, where $29 to $79 per month is the right shape for a weekly research cadence past 30 voice interviews a month
• BYO participants is the right fit: you already have users (or stakeholders), you just need to hear them

In practice, some teams will end up using both: Koji on a quarterly EU consumer panel where the data-residency story has to be airtight and the output is a printable report a stakeholder will actually read; Talkful inside the product for continuous signal on weekly decisions, in churn and cancel flows for the highest-honesty feedback a product team will ever get, and in pre-launch reviews where engineering, design, support, or legal weigh in before shipping. The two products solve adjacent jobs, on adjacent cadences, for partially overlapping buyers. The "vs" framing implies a single-winner shootout. The real question is what shape of evidence your next decision needs.

If you are still unsure, the Talkful Free plan is the honest way to check. Ten participants per month, full AI synthesis, no credit card. If the work is unambiguously "run a quarterly AI-moderated study on European consumers with strict EU data residency and a printable report at the end", the answer is Koji, not Talkful.

FAQ

Is Koji a direct competitor to Talkful?

Partially. Both ship AI-moderated async interviews and both produce structured output a downstream agent can act on. The overlap is real, and both products are aimed at PMs, UX researchers, and small product teams. The differences sit underneath: Koji optimizes for EU data residency, a structured six-type question catalog, a one-click thematic report after the study, and a credit-metered price model where each voice interview costs three credits. Talkful optimizes for researcher-picked probe depth (shallow, medium, or expert) per question, four modalities (voice, text, choice, rating) the participant picks per question, synthesis that streams while the study is still collecting, a flat workspace fee, and a single shareable link that lives across in-product, churn, post-onboarding, owned distribution, and internal stakeholder placements.

Does Koji have voice interviews? Does Talkful?

Both do. Koji runs AI-moderated voice and text interviews asynchronously, with adaptive follow-ups handled by the agent (reported at up to three per topic). Talkful runs AI-powered async interviews with smart follow-ups: after a participant submits a voice, text, or rating answer, a fast LLM decides whether one or more clarifying questions would sharpen the response, then shows each as a separate full-screen step the participant can answer in their preferred mode or skip. The researcher picks the depth per question (shallow, medium, or expert) as a methodology setting. The two interaction patterns are similar at the headline and different in the unit: Koji's agent runs a single conversational style end-to-end, Talkful's researcher controls the depth shape per question.

How do pricing and value compare on the entry paid tier?

Koji's Insights plan is €29/mo for 29 credits (≈ 9 voice interviews at 3 credits each, or 29 text chats at 1 credit). Koji's Interviews plan is €79/mo for 79 credits (≈ 26 voice interviews). Talkful Starter is $29/mo (annual) for 100 participants per month on the workspace, with unlimited studies, unlimited users, and full AI synthesis. Talkful Pro is $79/mo (annual) for 1,000 participants per month, with Slack integration and no Talkful branding. The dollar / euro figures rhyme at the entry tier; the shape of what they unlock is different. Koji sells per-conversation credits on AI-moderated interviews with EU data residency. Talkful sells a flat workspace fee on async user research with researcher-picked depth and continuous synthesis.

Which one has stronger EU data residency?

Koji, today. Koji is built in Amsterdam with EU data residency as a positioning headline. For a European insights team or a US team with European customers under GDPR scrutiny, that posture matters at procurement. Talkful runs on Supabase (Postgres), Cloudflare R2, and Vercel today, which is a different infrastructure shape with a different DPO conversation. If the procurement check is specifically "all customer voice and transcript data must remain on European infrastructure", Koji is the cleaner answer right now.

Can either tool feed the agents I build with Claude Code?

Both produce structured output your code can consume. Koji publishes one-click thematic reports with themes, recommendations, and traceable verbatim quotes; the platform also ships an MCP integration documented for Claude. Talkful exposes structured study output (themes, quotes, citations, audio anchors) through the dashboard and CSV / JSON exports, designed for the agents the team builds to act on, with Slack notifications as the published delivery channel today. Neither product ships a hosted MCP server over the live Talkful synthesis layer as of this writing. The integration shape is similar; the underlying evidence is different (AI-moderated interview transcripts vs in-context user answers on a research link).

Can I run both Koji and Talkful?

Yes, and that is the most defensible setup for a team that needs both jobs done. Koji on a quarterly AI-moderated consumer-research study where EU data residency, the structured question catalog, and a printable report are the right output. Talkful inside the product for continuous async signal on weekly decisions, in churn and cancel flows for the highest-honesty feedback a product team will ever get, and on internal stakeholder reviews where engineering, design, support, or legal weigh in before shipping. The tools solve adjacent jobs on adjacent cadences. The "vs" framing is more useful for SEO than for actual purchasing decisions.

The honest answer to "Koji vs Talkful" is that the buyer almost always settles it once they write down where the next interview is happening and what shape of output the team will actually read. If it is a quarterly AI-moderated study on European consumers under GDPR scrutiny, with a printable report at the end, that is a Koji problem. If it is one of your users answering a research question inside the product, on a cancel page, in a post-onboarding email, or one of your stakeholders weighing in on a prototype before launch, with synthesis streaming back as the answers land, that is a Talkful problem. Both products are right about their buyer. The expensive mistake is buying the wrong one for the research you actually need to do.