sales-skills/sales-frank

Frank Platform Help

Frank (hifrank.ai, built by Prelaunch.com) is an AI-moderated customer researcher that interviews your real customers — an AI voice (and chat) interviewer that adapts its follow-ups, runs many conversations in parallel 24/7, and turns them overnight into transcripts, themes, sentiment, key quotes, and video reels across 30+ languages. "Talk to hundreds of real customers while you sleep."

Its defining trait in the AI-moderated-interview cluster is bring-your-own-customers (BYOC) only — it has no third-party participant panel. You reach the people who already use (or are considering) your product by sharing an interview link — in an email, a chat, or as a step in the user's journey inside your product — with an optional incentive (an Amazon gift card, a discount, or nothing). That makes it the pick when you already have customers/prospects to talk to (churn, discovery, concept/feature testing, engagement), and the wrong pick when you need to reach strangers you don't already have.

Two things to say almost every time:

• It's real humans, but a stated "I'd pay" is still not a purchase. A real interview beats a synthetic "yes" (genuine reasoning, objections, language) — but the willingness-to-pay go/no-go belongs to a real behavior test (pre-sale, smoke test), not a transcript. Route that to /sales-idea-validation.
• Frank is UI-only for self-serve users. There is no public API, no webhooks, no Zapier, no MCP — an "API integration" is listed only on the Enterprise tier and is undocumented. Any "pipe transcripts into my CRM/warehouse" ask is manual export, not an integration. If the user needs an API-native interview pipeline, the right tool is /sales-userintuition, not Frank.

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated platform knowledge.

Ask only what you can't infer:

1. What do you want from Frank?
   • A) Set up a study — write the objective/discussion guide, pick voice vs chat, set incentive
   • B) Distribute — get the interview link in front of your own customers (email, chat, in-product)
   • C) Read results — themes, sentiment, quotes, video reels — or decide whether to trust them
   • D) Get data out — export transcripts/reels for a deck, CRM, or warehouse (no API — manual)
   • E) Choose a plan — Free vs Starter vs Growth vs Business by voice-minute allowance
   • F) Compare — Frank vs User Intuition / Strella / Listen Labs / Voicepanel for your use case
2. Do you already have customers/prospects to interview? Frank has no panel — if you need to reach strangers, it's the wrong tool (route to a panel-based peer or a recruit marketplace in Step 2).

Skip-ahead: if the user wants to compare research tools across the market or the validate-before-building method, that's a /sales-idea-validation question — route in Step 2.

Step 2 — Route or answer directly

| If the user's question is about… | Route to | |---|---| | Comparing research/idea/synthetic tools across the market, or the validate-before-building method | /sales-idea-validation {question} | | An API / webhook / MCP-native interview pipeline (Frank has none for self-serve) | /sales-userintuition {question} | | Reaching strangers on a recruited panel (Frank is BYOC-only, no panel) | /sales-respondent or /sales-idea-validation {question} | | Running a real behavior demand test (smoke-test page, waitlist, pre-sale) after interviews | /sales-idea-validation or /sales-funnel {question} | | Analyzing existing NPS/CSAT/VoC/review feedback (post-launch, not new interviews) | /sales-customer-feedback {question} | | A synthetic-persona peer (AI personas instead of real people) | /sales-syntheticusers or /sales-ditto {question} |

When routing, give the exact command: "This is a {domain} question — run: /sales-idea-validation {original question}"

Otherwise, answer Frank-specific questions using Step 3.

Step 3 — Frank platform reference

Read references/platform-guide.md for the full reference — the module/automation-surface table (what's exportable vs UI-only), best-effort pricing and the voice-minute/credit model, the Study → Interview → Participant → Insight data model, BYOC distribution, and the no-API data-out playbook. Frank has no public API for self-serve users, so there is no API-reference file.

Answer using only the relevant section — don't dump the full reference.

Step 4 — Actionable guidance

• Say the caveat: a real interview is not a purchase. Whatever the user asks, make explicit that Frank interviews real humans (a big step up from synthetic personas — genuine reasoning, objections, language), but a stated "I'd pay" in an interview is not observed demand. Keep the why/objections/ language and take the go/no-go from a real behavior test (smoke-test click, pre-sale) — route that to /sales-idea-validation. When they interpret results, point them to the verbatim quotes and video reels under each theme (the objections, hesitation, and pricing language) — not just the top-line sentiment/theme summary, which is an index, not the finding.
• Lead with BYOC: Frank has no panel. When the user asks who Frank interviews, state that it is bring-your-own-customers only — you distribute a share link to your own customers/prospects (email, chat, or an in-product journey step), so it fits churn/discovery/concept-testing with an audience you already have. If they need to reach strangers, say so plainly and route to a panel-based peer (/sales-userintuition, /sales-listenlabs) or a recruit marketplace (/sales-respondent).
• Flag it as UI-only when any automation/export comes up. Frank has no public API, webhooks, Zapier, or MCP for self-serve plans (an "API integration" appears only on the Enterprise tier and is undocumented — verify against current docs). "Sync transcripts to HubSpot/Snowflake" is a manual export/copy job. If the user needs a programmatic pipeline, tell them so and route to /sales-userintuition.
• Recommend a plan by voice-minute allowance, not seats. Frank meters voice minutes (roughly ~15 min/interview; ~10 credits ≈ 1 voice minute or ~10 chat messages), so size the plan by how many interviews/month they'll run, and note chat conversations are cheaper than voice. Present every price as best-effort and point to hifrank.ai/pricing to confirm.
• Keep the discussion guide tight; let the Research Agent draft it. Setup is fast (share your site + goal, the AI drafts an outline in ~2 min). Give it a sharp objective + a few core questions and let the adaptive moderator probe — a thin brief caps depth, but an over-long guide fatigues participants.
• Set the incentive to the ask. The incentive is optional (Amazon gift card, purchase discount, or none) — use a reward for cold/low-motivation audiences and skip it for engaged customers; it's a response-rate lever, not a quality guarantee.

If you discover a gotcha or tip not in references/learnings.md, append it there with today's date.

Gotchas

Best-effort from research (2026-07) — Frank is new (launched ~2026-04) and its modality mix, pricing, and (absent) API surface move fast; verify at hifrank.ai.

• Real ≠ demand. Interviews reveal the why better than any synthetic tool, but a stated intent to pay is not a purchase — the go/no-go still belongs to a real behavior test (pre-sale, smoke test).
• No panel — BYOC only. Frank does not recruit strangers; you must already have customers/prospects and distribute a share link. For strangers, use a panel-based peer or a recruit marketplace.
• No API, no webhooks, no Zapier, no MCP (self-serve). Frank is UI-only; "API integration" is an Enterprise-only, undocumented line item. The only data-out is manual export (transcripts, reels). Don't design a live integration around it — route pipeline needs to /sales-userintuition.
• Voice is the live modality; video + WhatsApp are newer/coming. Marketing lists voice, video, and WhatsApp, but voice (and chat) are what's shipped — treat video/WhatsApp as best-effort until confirmed.
• Plans meter voice minutes, and interviews have a max length. Free caps interview length short; paid tiers raise it. Model cost by minutes, not by interview count, and confirm the current caps on site.
• Pricing is published but moves. Monthly vs annual differ a lot (annual is far cheaper per month) — treat any figure as best-effort and confirm at hifrank.ai/pricing.
• It's built by Prelaunch.com. Frank is a distinct product from Prelaunch's concept-testing/pre-order platform — don't conflate their feature sets or pricing.

Related skills

• /sales-idea-validation — The tool-agnostic validate-before-building method + the full research/persona/validator tool landscape (use this to actually decide build-or-not; an interview "yes" is not demand). Install: npx skills add sales-skills/sales --skill sales-idea-validation -a claude-code
• /sales-userintuition — The API/webhook/MCP-native real-human interview peer — the pick when you need a programmatic pipeline Frank can't offer for self-serve (deepest developer surface in this cluster; also has a big panel Frank lacks). Install: npx skills add sales-skills/sales --skill sales-userintuition -a claude-code
• /sales-strella — The other UI-only real-human AI-moderated interview peer (deterministic scripted flows, 3M+ panel via User Interviews); pick Strella when you want a panel or scripted repeatability, Frank when you're interviewing your own customers self-serve. Install: npx skills add sales-skills/sales --skill sales-strella -a claude-code
• /sales-listenlabs — The enterprise/consultative real-human peer with a 30M+ panel, REST API v2 + OAuth MCP; pick it when you need a panel of strangers or an API pipeline. Install: npx skills add sales-skills/sales --skill sales-listenlabs -a claude-code
• /sales-chikka — The closest analog — another self-serve, UI-only AI voice-interview tool with a free tier; its twist is a voice → marketing-asset pipeline (VoiceVerify testimonials/case studies). Pick by pricing model and whether you want testimonials out of the same interview. Install: npx skills add sales-skills/sales --skill sales-chikka -a claude-code
• /sales-respondent — Participant-recruitment marketplace — recruits, screens, and pays strangers from a 4M+ B2B/professional panel; the pick when Frank's BYOC model can't reach the audience you need. Install: npx skills add sales-skills/sales --skill sales-respondent -a claude-code
• /sales-customer-feedback — Real-customer NPS/CSAT/VoC strategy across tools (use to analyze existing feedback; Frank proactively runs new interviews with your own customers). Install: npx skills add sales-skills/sales --skill sales-customer-feedback -a claude-code
• /sales-funnel — Build the smoke-test / fake-door landing page that turns an interview hypothesis into a real demand test. Install: npx skills add sales-skills/sales --skill sales-funnel -a claude-code
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do -a claude-code

Examples

Example 1: "The interviews said they'd pay — should I build it?"

User says: "I ran 30 Frank interviews with my waitlist and most said they'd pay. Green light?" Skill does: Credits the signal as real (actual humans, genuine reasoning — far better than a synthetic tool), then draws the line: a stated "I'd pay" is not a purchase. Tells the user to open the verbatims and video reels and mine objections, hesitation, and pricing language, then take the go/no-go from a real behavior test (a pre-sale or a "buy" click) via /sales-idea-validation. Flags pricing as best-effort and points to hifrank.ai/pricing. Result: The founder keeps the qualitative depth and runs a pre-sale before committing to the build.

Example 2: "How do I get Frank transcripts into HubSpot automatically?" (developer/automation)

User says: "I want every completed Frank interview transcript to land on the HubSpot contact." Skill does: States plainly that Frank is UI-only for self-serve — no public API, webhooks, Zapier, or MCP (an "API integration" is an Enterprise-only, undocumented line item), so there's no automated path; the only data-out is a manual export of transcripts/reels. If a live pipeline is a hard requirement, routes to /sales-userintuition (REST API + HMAC webhooks + MCP) as the API-native alternative, noting the trade-off (Frank's edge is fast, cheap self-serve interviews with your own customers). Result: The user stops hunting for an API that doesn't exist and either exports manually or switches tools for the pipeline.

Example 3: "Which plan do I need, and how do I reach people to interview?"

User says: "I want ~25 customer interviews a month. Which Frank plan, and where do the participants come from?" Skill does: Explains Frank is BYOC — no panel: you share an interview link with your own customers/prospects (email, chat, or in-product journey), with an optional incentive. Sizes the plan by voice-minute allowance (~15 min/interview → ~25 interviews needs a mid tier's minute pool; chat is cheaper than voice), flags monthly-vs-annual pricing as best-effort → hifrank.ai/pricing, and notes that if they need to reach strangers they don't already have, Frank can't — route to /sales-respondent or a panel-based peer. Result: The user picks a plan by minutes and understands they must supply their own audience.

Troubleshooting

"Who does Frank interview — where's the panel?"

Symptom: The user expects Frank to recruit participants like a panel tool. Cause: Frank is bring-your-own-customers only — it has no third-party panel. It interviews the people you already have a way to reach. Solution: Distribute the interview link to your own customers/prospects (email, chat, or an in-product journey step), with an optional incentive (Amazon gift card / discount / none). If you need to reach strangers, Frank is the wrong tool — recruit via /sales-respondent or use a panel-based peer (/sales-userintuition, /sales-listenlabs).

"How do I automate exports / connect Frank to my stack?"

Symptom: You want transcripts, reels, or insights flowing into a CRM, warehouse, or Slack. Cause: Frank has no public API, webhooks, Zapier, or MCP for self-serve plans — it's UI-only (an "API integration" appears only on the Enterprise tier and is undocumented; verify against current docs). Solution: Use the built-in manual export (transcripts / video reels / summaries) for decks and ad-hoc handoff. If an automated, programmatic pipeline is a requirement, use an API-native interview tool instead — route to /sales-userintuition (REST API + HMAC-signed webhooks + MCP server).

"Which plan, and why did I run out of interviews so fast?"

Symptom: The user is unsure which tier to buy, or hit a limit sooner than expected. Cause: Frank meters voice minutes (roughly ~15 min/interview; ~10 credits ≈ 1 voice minute or ~10 chat messages) and caps interview length by tier — so cost tracks minutes, not interview count, and a few long voice interviews burn a small plan. Solution: Size the plan by monthly voice-minute allowance for your expected interview count, prefer chat where voice depth isn't needed (it's cheaper), and watch the per-tier max interview length. Treat every figure as best-effort and confirm at hifrank.ai/pricing.