---
name: sales-evelance
description: "Evelance (evelance.io) platform help — a synthetic-persona (AI) consumer message/creative testing tool: describe an audience and AI personas evaluate your messaging, website, app, prototype, or design file, returning 12 consumer-psychology scores plus written per-persona rationale and AI-synthesis reports in ~10–30 min, no recruiting — pitched as a fast, cheap PickFu alternative, but synthetic (an AI opinion), not a real panel. A/B and competitive tests; pay-as-you-go per persona (volume packs), free 5-day trial. Use when setting up an Evelance test, picking an audience, reading its scores and persona rationale, budgeting personas, comparing message variants, getting data out when there's no public API, or judging whether a synthetic score counts as demand. Do NOT use for the validate-before-building method or comparing research tools (use /sales-idea-validation), real-panel message testing (use /sales-pickfu or /sales-wynter), or synthetic usability testing on a built product (use /sales-uxia)."
argument-hint: "[describe what you need help with in Evelance]"
license: MIT
version: 1.0.0
tags: [sales, market-research, platform]
---

# Evelance Platform Help

Evelance (evelance.io) is a **synthetic-persona (AI) consumer-research and message/creative-testing** tool.
You describe a target audience, and Evelance matches **AI personas** (a claimed 326M+ predictive personas
across 23 countries; 800M+ on Enterprise) that "read" your **messaging, copy, website, app, prototype, or
design file** and return **12 consumer-psychology scores** (attention, trust, value, emotion, action-readiness,
…) on a **10-point scale**, **written per-persona rationale** (what they understood, what confused them, "the
exact line where readers got lost"), and **AI-synthesis reports** — usually in **~10–30 minutes**, with **no
recruiting**. It runs **A/B** and **competitive** tests and is pitched as a fast, cheap **PickFu alternative**.

Its niche vs the rest of the research/validation cluster:
- **Synthetic, not a real panel.** The whole pitch is *speed without recruiting* — AI personas, not real
  people. That makes it the **cheap/fast pre-flight** counterpart to real-panel testers (`/sales-pickfu`
  consumer, `/sales-wynter` B2B), and a sibling of synthetic usability (`/sales-uxia`) — an **AI opinion**,
  not observed behavior.
- **Message/creative/product testing, not idea validation or usability depth.** Evelance tells you whether
  your *words and creative* land with a modeled audience — not build-or-not (`/sales-idea-validation`) and
  not deep usability metrics on a built flow (`/sales-uxia` / `/sales-uxtweak`).
- **Self-serve, pay-as-you-go per persona.** ~$2.99/persona (volume packs cheaper), a free 5-day trial
  (10 personas, no card), Enterprise custom. A solo maker can run a test today.

**Two things to say almost every time (the double caveat):**
- **A synthetic score is an AI opinion, not real behavior.** Personas are trained on *averaged* data, so they
  can't capture individual variation, they inherit **bias amplification**, and even Evelance says they don't
  replace real research. Trust **relative** comparisons (variant A vs B) over **absolute** scores, and
  **validate the winner with real people**. The vendor's **89.78% "thematic-accuracy"** figure is a
  self-reported study (23 humans vs 7 personas) — treat it as best-effort.
- **Even real resonance is not demand.** "This message scored high / personas said they'd act" is what *lands
  with a reader-model*, **not** a purchase. Keep the winning message + the rationale; take the
  willingness-to-pay / conversion go/no-go to a **real behavior test** (smoke test, pre-sale) via
  `/sales-idea-validation`.

## 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 Evelance?**
   - A) **Set up a test** — pick what you're testing (messaging/copy, website, app, prototype, design file)
     and the test mode (single, **A/B**, **competitive**)
   - B) **Describe the audience** — the buyer/user you want personas matched to
   - C) **Read results** — the 12 psychology scores, per-persona written rationale, and the synthesis report
   - D) **Budget** — personas per test, pay-as-you-go vs packs, what a test costs
   - E) **Get data out** — export/automation (there is **no API/webhooks/MCP**)
   - F) **Choose** — Evelance (synthetic) vs a real panel (`/sales-pickfu` / `/sales-wynter`) or synthetic
     usability (`/sales-uxia`)
2. **Is a decision riding on this, or is it a fast pre-flight?** This decides how hard to push the "synthetic
   is directional; validate the winner with real people / real behavior" message.

Skip-ahead: if the user wants the **validate-before-building method** or a **cross-tool comparison**, 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 |
|---|---|
| The **validate-before-building method**, or comparing research/validation tools across the market | `/sales-idea-validation {question}` |
| A **REST API / webhook / MCP**-native research pipeline (Evelance has none — it's UI-only) | `/sales-userintuition {question}` |
| Running a **real behavior** demand test (smoke-test page, waitlist, pre-sale) after a synthetic test | `/sales-idea-validation` or `/sales-funnel` `{question}` |
| **Real-panel** consumer creative/message split-testing (real people vote + explain) | `/sales-pickfu {question}` |
| **Real-panel B2B** message/positioning testing (verified professionals in your ICP) | `/sales-wynter {question}` |
| **Synthetic usability** testing on a *built* product/prototype (SUS/heatmaps, not message scores) | `/sales-uxia {question}` |
| Analyzing **existing** NPS/CSAT/VoC/survey data into themes (post-collection) | `/sales-customer-feedback` or `/sales-trill` `{question}` |

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

Otherwise, answer Evelance-specific questions using Step 3.

## Step 3 — Evelance reference

**Read `references/platform-guide.md`** for the full reference — the test-type / automation-surface table
(everything is UI-only), the 12 psychology-score model, best-effort per-persona pricing and plan gates, the
Test → Audience → Persona-response data model, how to write a clean audience + variant set, and how to read
relative scores + written rationale (not absolute numbers).

There is **no `evelance-api-reference.md`** — Evelance has **no public API/webhooks/MCP** (best-effort;
confirm at evelance.io). Don't invent endpoints.

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

## Step 4 — Actionable guidance

- **Lead with the double caveat: synthetic = AI opinion, and resonance ≠ demand.** Whatever the user asks,
  make explicit that (1) Evelance personas are **synthetic — an AI opinion trained on averaged data** (bias
  amplification, no individual variation), so a high score is **not real behavior**; and (2) even real message
  resonance is **stated preference, not a purchase**. Both caveats stack: trust the **written per-persona
  rationale** and **relative** rankings, validate the winner with **real people**, and take the
  willingness-to-pay / conversion go/no-go to a **real behavior test** via `/sales-idea-validation`.
- **Trust relative comparisons over absolute scores.** Steer the user to read Evelance as a **ranking engine**
  — which variant/message the personas prefer and *why* — not as a calibrated absolute score. Use the 12
  psychology scores to **compare A vs B or vs a competitor**, and mine the per-persona rationale for the
  objection/confusion language. Do not report an absolute "5.8/10" as if it were a validated metric.
- **Treat automation as UI-only, and don't invent endpoints.** When any API/webhook/export/integration comes
  up, state that Evelance has **no public REST API, webhooks, Zapier/Make, or MCP server** (best-effort —
  confirm at evelance.io); data-out is **manual export** from the dashboard. Don't design a REST/webhook
  pipeline or guess endpoints; if a programmatic pipeline is a hard requirement, route to `/sales-userintuition`.
- **Size a test by persona count, and call pricing best-effort.** Cost scales with **how many personas** you
  run (~$2.99/persona pay-as-you-go, cheaper in volume packs; a 10-persona test ≈ $23.90–$29.90). Give a
  directional figure but present every number as **best-effort** and point to evelance.io/pricing; note the
  **free 5-day trial (10 personas, no card)** for a first run. More personas = tighter averages, not more
  truth — a synthetic sample doesn't become demand by getting bigger.
- **Match the test to the job, and describe the audience well.** Recommend **messaging/copy testing** for "does
  my headline/value-prop land", **A/B** for picking between variants, **competitive** for reading against a
  rival's message, and **website/app/prototype/design-file** tests for creative — and reserve Evelance for the
  **fast, cheap synthetic pre-flight**, routing a real-panel decision to `/sales-pickfu` / `/sales-wynter` and
  build-or-not to `/sales-idea-validation`. A vague audience description yields generic personas — describe
  the buyer/user specifically.

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) — Evelance's pricing (per-persona rate, packs), persona-pool size,
score model, test-type lineup, and export options change; verify at evelance.io and evelance.io/pricing.*

- **Synthetic ≠ real behavior.** Personas are an **AI opinion** trained on averaged data — they inherit
  **bias amplification**, can't surface a *new* individual reaction, and don't replace real research (the
  vendor agrees). Trust **relative** rankings + the written rationale; validate the winner with real people.
- **Resonance ≠ demand.** A high score or a persona "I'd act on this" is what lands with a *reader-model*, not
  a purchase. Take the go/no-go to a real behavior test (smoke test, pre-sale) via `/sales-idea-validation`.
- **The 89.78% accuracy figure is a vendor claim.** It's a self-reported study (23 real people vs 7 personas)
  — directional evidence the tool tracks *some* real reactions, not a guarantee for *your* audience.
- **No public API/webhooks/MCP/Zapier — automation is manual export.** Data-out is dashboard export; don't
  invent endpoints or design a webhook pipeline (that's a `/sales-userintuition` job). Best-effort — confirm
  at evelance.io.
- **Priced per persona; more personas isn't more truth.** ~$2.99/persona PAYG (cheaper in packs); cost scales
  with persona count. A bigger synthetic sample tightens the average, it doesn't turn an AI opinion into demand.
- **It's synthetic message/creative testing, not usability or idea validation.** For a *built* product/
  prototype use `/sales-uxia` (synthetic) or `/sales-uxtweak` (real); for real-panel message testing use
  `/sales-pickfu` (consumer) / `/sales-wynter` (B2B); for build-or-not use `/sales-idea-validation`.
- **Absolute scores aren't calibrated.** Read Evelance as a comparison/ranking tool — an absolute "5.8/10"
  is not a validated metric; the value is A-vs-B and the per-persona *why*.

## Related skills

- `/sales-idea-validation` — The tool-agnostic validate-before-building method + the full research/validator tool landscape (use to decide build-or-not; a synthetic "yes" is an AI opinion, not demand). Install: `npx skills add sales-skills/sales --skill sales-idea-validation -a claude-code`
- `/sales-pickfu` — PickFu, the **real-panel consumer** counterpart (15M+ real people vote *and* explain why, with a real REST API/MCP/CLI). Pick PickFu for a real-panel decision; pick Evelance for a fast/cheap synthetic pre-flight. Install: `npx skills add sales-skills/sales --skill sales-pickfu -a claude-code`
- `/sales-wynter` — Wynter, the **real-panel B2B** message-testing counterpart (80k+ LinkedIn-verified professionals in your ICP). Pick Wynter for real B2B positioning signal; Evelance for a synthetic pre-flight. Install: `npx skills add sales-skills/sales --skill sales-wynter -a claude-code`
- `/sales-uxia` — Uxia, the **synthetic usability-testing** sibling (AI testers on a built design/prototype → SUS/heatmaps) — the right synthetic tool when the question is "is my product usable", not "does my message land". Install: `npx skills add sales-skills/sales --skill sales-uxia -a claude-code`
- `/sales-userintuition` — The REST-API/webhook/MCP research peer — the pick when you need a programmatic pipeline Evelance's UI-only surface can't offer. Install: `npx skills add sales-skills/sales --skill sales-userintuition -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: "Evelance scored my new headline 8.2/10 — should I launch on that?"
**User says**: "I ran my homepage headline through Evelance and 10 personas gave it 8.2/10 on trust and value, and most said they'd sign up. Green light?"
**Skill does**: Names the **double caveat** — the personas are **synthetic (an AI opinion trained on averaged
data)**, so 8.2/10 is **not real behavior**, and even if it were, resonance is **not demand**. Reframes it as a
**relative ranking + rationale**, not a calibrated score, and routes the user to **validate the winner with real
people** (`/sales-pickfu`) and a **real behavior test** (smoke-test/pre-sale) via `/sales-idea-validation`.
**Result**: The user keeps the winning message + rationale and earns the go/no-go from real behavior, not a synthetic score.

### Example 2: "How do I pull Evelance results into my dashboard automatically?" (developer/automation)
**User says**: "I want a webhook to fire when an Evelance test finishes and POST the psychology scores into our BigQuery. What's the endpoint?"
**Skill does**: States plainly that Evelance has **no public REST API, webhooks, Zapier/Make, or MCP server**
(best-effort — confirm at evelance.io), so there's **no endpoint or key to build against**, and does **not**
invent one — the only data-out is **manual export**. Routes an automated completion-to-warehouse pipeline to
`/sales-userintuition` (API + webhooks + MCP).
**Result**: The user stops hunting for an API that doesn't exist and either exports manually or moves the pipeline to an API-native tool.

### Example 3: "Evelance or PickFu for testing my Amazon main image?"
**User says**: "I've got two product main-image options. Should I use Evelance or PickFu to pick the winner?"
**Skill does**: Frames the trade-off — **Evelance is synthetic** (AI personas, ~10–30 min, an AI opinion) and
**PickFu is a real consumer panel** (15M+ real people vote *and* explain). Recommends Evelance as a **fast/cheap
pre-flight** to triage options, then a **real-panel** PickFu poll (`/sales-pickfu`) for the actual pick, and
reminds that even a PickFu winner is **preference, not demand** — the real proof is click-through/conversion.
**Result**: The user uses synthetic to triage cheaply and a real panel to decide, keeping demand as a separate question.

## Troubleshooting

### "Can I trust the Evelance score — is it accurate?"
**Symptom**: The user wants to know whether the 12 psychology scores reflect how real people will react.
**Cause**: Personas are **synthetic** — LLM models trained on *averaged* audience data. The **89.78% "thematic-accuracy"** is a self-reported study (23 humans vs 7 personas); averaged models can't capture individual variation and inherit **bias amplification**.
**Solution**: Use the scores as a **relative ranking** (A vs B vs competitor) and mine the **per-persona written rationale** — not as a calibrated absolute. **Validate the winner with real people** (`/sales-pickfu` / `/sales-wynter`) and take the go/no-go to a real behavior test (`/sales-idea-validation`).

### "Where's the Evelance API / webhook to export results?"
**Symptom**: You're looking for an API key and a webhook to fire when a test completes.
**Cause**: Evelance is **UI-only** — **no public REST API, webhooks, Zapier/Make, or MCP server** (best-effort; confirm at evelance.io). Data-out is **manual export**.
**Solution**: Export from the dashboard. For a programmatic warehouse/CRM pipeline, use an API-native tool — route to `/sales-userintuition`. Don't build against guessed endpoints.

### "How much will an Evelance test cost?"
**Symptom**: The user wants to budget a test.
**Cause**: Evelance bills **per persona** (pay-as-you-go, cheaper in volume packs), so cost scales with how many personas you run — a 10-persona test is roughly the price of ten personas (best-effort).
**Solution**: Size the test by **persona count**; start with the **free 5-day trial (10 personas, no card)** and confirm current rates at evelance.io/pricing. A bigger synthetic sample tightens the average but does **not** make an AI opinion into demand.