---
name: sales-atypica
description: "atypica.AI (atypica.ai) platform help — an AI research-agent platform that simulates consumers: build AI Personas from a four-layer \"subjective world model\", run AI-moderated Deep Interviews and studies against 300K synthetic + ~10K interview-grounded \"real person\" agents, and get an insights report in minutes. Its public API is a team-provisioning + embedding API (Bearer team key; impersonation login URLs to embed studies in your app), NOT a studies-execution API — studies run in the UI. Use when setting up an atypica study or AI Persona, running a concept/pricing/message test against synthetic consumers, embedding atypica in your product via the impersonation API, provisioning team members via the API, reading the 85%-human-likeness or flywheel claims critically, or deciding whether a synthetic 'yes' is real demand. Do NOT use for comparing synthetic-research/persona/idea tools across the market (use /sales-idea-validation) or analyzing real-customer NPS/VoC feedback (use /sales-customer-feedback)."
argument-hint: "[describe what you need help with in atypica]"
license: MIT
version: 1.0.0
tags: [sales, pre-launch, platform]
---

# atypica.AI Platform Help

atypica.AI (atypica.ai) is an **AI research-agent platform that simulates
consumers**. You build **AI Personas** on a four-layer **"subjective world
model"** (Expression → Story → Cognition → Behavior), run **AI-moderated
interviews and studies** against them, and get an insights report in minutes — a
synthetic-research alternative to a traditional agency or focus group. Its cluster
edge is that persona construction and a **flywheel** (validate synthetic personas
against real interviews) aim beyond plain LLM roleplay, plus **~10K
interview-grounded "real-person" agents** alongside the 300K synthetic ones.

Its signal is **directional, not demand**: personas model existing/social/
interview/training data, so they skew **agreeable, generic, and Western-biased**
and can be confidently wrong. Treat any synthetic result as a **hypothesis to
disconfirm with real behavior**, never a green light.

## 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 atypica?**
   - A) **Build a persona / run a study** — set up an AI Persona or an AI Interview /
     study, and read the insights report
   - B) **Embed atypica** in your own app (mint an impersonation login URL so a user
     lands in a study with no separate login)
   - C) **Provision team access** via the API (list / create / invite members)
   - D) **Interpret** a study/report you already ran, or decide **whether to trust it**
2. **What's the research question and who's the audience?** A sharp objective + a
   well-defined persona yields usable answers; a vague one yields generic filler.

Skip-ahead: if the user wants to **compare** synthetic-research/persona/idea 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 synthetic-research/persona/idea tools, or the validate-before-building **method** | `/sales-idea-validation {question}` |
| Running a **real** demand test (smoke-test page, waitlist, pre-sale) after synthetic signal | `/sales-idea-validation` or `/sales-funnel` `{question}` |
| Analyzing **real-customer** NPS/CSAT/VoC feedback (post-launch) | `/sales-customer-feedback {question}` |
| A census-calibrated demographic panel with a **free-tier API**, or a documented **studies** REST API | `/sales-ditto` or `/sales-syntheticusers` `{question}` |

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

Otherwise, answer atypica-specific questions using Step 3.

## Step 3 — atypica platform reference

**Read `references/platform-guide.md`** for the full reference — the three
capabilities + flywheel, the module → automation-surface table (what's API-
accessible vs UI-only), best-effort token/enterprise pricing, the data model (JSON
shapes for the team/embedding API), and quick-start recipes.

For raw endpoints, auth, JSON schemas, and error codes, read
`references/atypica-api-reference.md`.

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

## Step 4 — Actionable guidance

- **A synthetic "yes" is not demand — say so every time.** Whatever the user asks,
  make explicit that atypica's personas model existing/interview/training data, so
  they run **agreeable, generic, and Western-biased** and can be **confidently
  wrong**. Frame every study result as a **hypothesis to disconfirm with a real
  behavior test** (smoke-test click, reply, pre-sale) — keep the *objections,
  segments, and language* it surfaces (its real value), take the go/no-go from real
  strangers, and route the real test to `/sales-idea-validation`.
- **The public API is a team + embedding API, not a studies API — never invent
  study endpoints.** There is **no documented endpoint to create a persona or run a
  study** over HTTP; studies run in the app UI. Base **`https://atypica.ai/api`**,
  auth **`Authorization: Bearer <team-scoped key>`**. The only endpoints are `GET
  /team/members`, `POST /team/members/create`, `POST /team/members/invite`, and
  `POST /team/members/:userId/impersonation`. If the user asks to "run a study via
  the API," correct the premise and offer the embedding path instead.
- **To automate/embed a study, use the impersonation login URL.** `POST
  /team/members/:userId/impersonation` with `callbackUrl: "/newstudy"` returns a
  `loginUrl` you redirect/iframe so the user lands **authenticated on the new-study
  screen** with no separate atypica login. Honor `expiryHours` (default ~24) — mint
  a fresh URL per session, don't cache. The user must be a **team member first**
  (create or invite). Pull the exact request/response from the references.
- **Present the accuracy and flywheel claims as best-effort, cite the source.** The
  **85% human-likeness**, **100× cheaper**, and validate-against-real-interviews
  flywheel are **vendor-reported**; the four-layer "subjective world model" is a
  real differentiator over pure-LLM roleplay but calibrates *who the persona is*,
  not whether real buyers act. Say figures are best-effort and point to atypica.ai.
- **Present pricing as best-effort — it's token-metered.** Freemium includes
  **1,000,000 free tokens**; there's an **enterprise SOC2** tier. The `/pricing`
  page is JS-rendered — confirm current tiers on the live site before relying on them.
- **Sharpen the persona and objective — output tracks input.** A vague audience or
  question yields generic filler; recommend a specific objective and (where useful)
  **importing real interview transcripts** to ground a private persona library
  before running the study.

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) — pricing (token counts), the accuracy claims,
persona counts, and the API surface move; verify at atypica.ai and atypica.ai/docs/api.*

- **Sycophancy / agreeableness bias.** Personas skew *favorable* — they can approve
  a concept or price real buyers would reject. Never read a positive synthetic
  reaction as validation.
- **Simulated ≠ real behavior.** The four-layer world model matches how a persona
  *is described*, not the messy choices of a real person — and it can't surface a
  genuinely *new* unmet need. The "expression-behavior gap" is a calibration signal,
  not a fix for it.
- **Western/English and identity-flattening bias.** Outputs skew Western/majority
  and can flatten segments — be cautious generalizing across markets or narrow niches.
- **The API can't run studies.** It's a **team-provisioning + embedding** API only.
  Don't promise "run a study via the API" — automate by embedding via an
  impersonation login URL; studies execute in the UI.
- **Impersonation URLs expire and are identity-scoped.** They honor `expiryHours`
  (default ~24) and require a real team member (`403` if the email domain isn't
  whitelisted for `create`). Mint per session; use `invite` for users on other domains.
- **Vendor accuracy figures.** "85% human-like", "100× cheaper", the flywheel
  validation — all vendor-reported. Cite them as claims, not proof.

## Related skills

- `/sales-idea-validation` — The tool-agnostic validate-before-building method + the full synthetic-research/persona/validator tool landscape (use this to actually decide build-or-not; a synthetic "yes" is not demand)
- `/sales-syntheticusers` — Synthetic Users platform help (the pure-play interview-study peer with a **documented public REST studies API** [Python/TS SDKs, SSE streaming]; atypica's API is team/embedding-only)
- `/sales-ditto` — Ditto platform help (census-calibrated demographic panels with a **free-tier studies API** + official Claude Code skills; atypica has richer persona construction but no studies API)
- `/sales-imario` — iMario platform help (the reusable-persona canvas — a Synthetic Individual with cross-session memory across six jobs; atypica's flywheel validates personas against real interviews)
- `/sales-societies` — Artificial Societies platform help (the self-serve network simulator that predicts how a post/message *spreads*; atypica asks isolated personas, Societies models influence between them)
- `/sales-customer-feedback` — Real-customer NPS/CSAT/VoC strategy across tools (use once you have live customers; synthetic research is pre-launch only)
- `/sales-funnel` — Build the smoke-test / fake-door landing page that turns a synthetic hypothesis into a real demand test
- `/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: "atypica's synthetic consumers loved my concept — should I build?"
**User says**: "My atypica study says 85% human-like and the personas loved the idea. Green light?"
**Skill does**: Explains atypica's personas **model existing/interview/training data
and skew agreeable**, so an enthusiastic reaction is a **directional signal, not
demand**, and flags the **85% figure as a vendor claim** (calibration, not
validity). Tells the user to **mine the report for objections and language** (the
real value) and take the go/no-go from a **real behavior test** — a smoke test or
pre-sale — routed via `/sales-idea-validation`.
**Result**: The founder keeps the objections, runs a real pre-sale before committing.

### Example 2: "Run a persona and a study from my script via the API" (developer/automation)
**User says**: "How do I use the atypica API to create a persona and run a research study programmatically?"
**Skill does**: Corrects the premise — the public API is a **team-provisioning +
embedding API**, with **no endpoint to create a persona or run a study**; studies
run in the UI. Gives base `https://atypica.ai/api` + `Bearer` team key, and offers
the real path: **embed atypica** via `POST /team/members/:userId/impersonation`
(`callbackUrl:"/newstudy"`) so the user lands in a study in your app. Points to
`references/atypica-api-reference.md`; refuses to invent study endpoints.
**Result**: The user embeds a study flow instead of chasing a nonexistent studies API.

### Example 3: Embed a study in my product with no separate login (references/platform-guide.md)
**User says**: "I want users of my app to run an atypica study without logging into atypica separately."
**Skill does**: Points to the **impersonation login** endpoint — provision/invite
the user as a **team member**, then `POST /team/members/{userId}/impersonation`
with `expiryHours` + `callbackUrl:"/newstudy"` returns a `loginUrl` to redirect or
iframe; the user arrives authenticated on the new-study screen. Notes the token
**expires** (default ~24h, mint per session) and a `403` means the domain isn't
whitelisted (use `invite`). Pulls the Python snippet from the platform guide.
**Result**: The user ships an embedded, no-extra-login atypica study flow.

## Troubleshooting

### "The personas agreed with everything — is that real signal?"
**Symptom**: Study answers are uniformly positive and agreeable.
**Cause**: LLM-based personas exhibit **sycophancy** and model plausible agreement —
the four-layer world model fixes *who* they are, not the agreeableness bias.
**Solution**: Discard the "verdict," keep the **objections and language** surfaced,
and validate with **real behavior** (smoke test, pre-sale). Sharpen the objective
and persona (ground it in real interview transcripts) to cut generic agreement.
Cross-check via `/sales-idea-validation`.

### "I can't find the API endpoint to launch a study"
**Symptom**: The docs only show `/team/members*` — no way to create a persona or run a study.
**Cause**: The public API is a **team-provisioning + embedding** API; studies are a
**UI operation**, not an HTTP endpoint.
**Solution**: Run studies in the app, or **embed** the flow with an impersonation
login URL (`POST /team/members/:userId/impersonation`, `callbackUrl:"/newstudy"`).
Don't invent study endpoints. See `references/atypica-api-reference.md`.

### "My create-member call returns 403 / the impersonation URL stopped working"
**Symptom**: `POST /team/members/create` returns `403`, or a saved `loginUrl` no longer authenticates.
**Cause**: `403` = the email **domain isn't whitelisted** for the team; a dead login
URL = the impersonation **token expired** (`expiryHours`, default ~24).
**Solution**: For users on an unverified domain use `POST /team/members/invite`
(existing registered users) instead of `create`; **mint a fresh impersonation URL
per session** rather than caching it. See `references/atypica-api-reference.md`.