---
name: sales-societies
description: "Artificial Societies (societies.io) platform help — a synthetic-audience simulator that models a target audience as a network of AI personas who influence each other on a social graph, so you can pre-test how a post spreads and predict engagement before publishing. Its self-serve product simulates your LinkedIn audience, scores content, and returns results in ~30s-2min; the enterprise Radiant tier builds 300-5,000-persona societies from first-party CRM/research data. Use when running a Societies simulation, pre-testing a LinkedIn/marketing post, interpreting its engagement score or the R-squared 0.78 / 95%-accuracy claims, or choosing Free (3 credits) vs paid Pro vs enterprise. UI-only — no public API, webhooks, or Zapier; a predicted engagement score is a directional pre-test, not real demand. Do NOT use for comparing synthetic-research/validator tools or the validate-before-building method (use /sales-idea-validation), or the pure-play interview-study tool with a public API (use /sales-syntheticusers)."
argument-hint: "[describe what you need help with in Artificial Societies]"
license: MIT
version: 1.0.0
tags: [sales, validation, pre-launch, platform]
---

# Artificial Societies Platform Help

Artificial Societies (**societies.io**, YC W25) is a **synthetic-audience simulator**. Where the other
synthetic-research tools run *interviews* against independent AI participants ([[sales-syntheticusers]],
[[sales-imario]]) or let you *chat* with one data-grounded persona ([[sales-delve]], Marketing Mary),
Societies models a **whole audience as a network** — 300 to 5,000+ AI personas placed on a **social
graph** who **react to your content and influence each other**, so the output is how a message
**spreads and how opinion propagates**, not just isolated reactions. Each run takes **~30s–2 min**.

Its self-serve product (the LinkedIn-audience simulator, "Reach") is built for **pre-publishing a post
or message**: pick or build a target audience, paste your content, and get an **engagement score** plus
alternate variations to compare — its headline claim is an **R²=0.78** fit to real LinkedIn engagement
(personas are built from LinkedIn data). The enterprise **Radiant** tier builds purpose-built societies
from **first-party CRM/qual/quant data** with follow-up qualitative interviews, SSO/SCIM, SOC2/GDPR.

**It is not demand.** The founders say it plainly — *"synthetic audiences should never replace listening
to real people."* A predicted engagement score is a **directional pre-test** that can produce **false
positives** (a bad message can still score high). Use it to shortlist and sharpen; earn the go/no-go
from real behavior.

## 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 Artificial Societies?**
   - A) Run a simulation and interpret the result — the engagement score, the network reactions, the variations
   - B) Pre-test a specific LinkedIn/marketing post or message before publishing
   - C) Understand the accuracy claims (R²=0.78, 95% opinion accuracy, 90% coherence) and how much to trust them
   - D) Pick a plan — Free (3 credits) vs Pro ($40/mo) vs Team vs enterprise Radiant
   - E) Automate/export programmatically (API reality)
2. **What's the real question — "how do I use this tool?" or "should I build/ship this?"** If it's the
   go/no-go decision or a validator comparison, that's `/sales-idea-validation` (a synthetic score is not
   demand) — route in Step 2.
3. **Self-serve or enterprise?** The $40/mo self-serve product tests *content/messaging*; Radiant builds
   data-grounded societies for larger studies. Answer for the tier the user is actually on.

Skip-ahead: if the user wants to compare synthetic-research tools or the validate-before-building
*method*, route to `/sales-idea-validation` immediately.

## Step 2 — Route or answer directly

| If the user's question is about… | Route to |
|---|---|
| Comparing Societies vs other synthetic-research/persona/validator tools, or the go/no-go decision | `/sales-idea-validation {question}` |
| The pure-play **synthetic-interview study** tool (multi-participant studies, with a real public API) | `/sales-syntheticusers {question}` |
| A **reusable Synthetic Individual** across many jobs (memory, Pro-gated API) | `/sales-imario {question}` |
| Building the real **smoke-test landing page** to measure demand | `/sales-funnel {question}` |
| Actually **posting/scheduling** the content once it's tested (LinkedIn/social) | `/sales-social-media-management {question}` |
| Turning tested messaging into a real **content** program | `/sales-content {question}` |

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

Otherwise, answer Societies-specific questions using Step 3.

## Step 3 — Artificial Societies platform reference

**Read `references/platform-guide.md`** for the full reference — the two products (self-serve
LinkedIn/audience simulator vs enterprise Radiant), how a simulation works (persona network on a social
graph, opinion propagation, ~30s–2 min runs, content variations), how to define an audience
(demographics / professional targeting / first-party data), the accuracy claims and their caveats
(R²=0.78 LinkedIn, 95% opinion-distribution, 90% coherence — all vendor self-reported), the pricing
tiers (Free 3 credits → Pro $40/mo → Team → enterprise Radiant), and the **no-public-API** automation
reality plus how to read a synthetic result honestly.

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

## Step 4 — Actionable guidance

- **Treat the engagement score as a directional pre-test, not demand.** The score is a **synthetic
  prediction** — personas modeled from public/social data reacting to your content. It's genuinely useful
  for **A/B pre-testing messaging** (which of two hooks lands, where an argument loses people), and the
  R²=0.78 LinkedIn fit is a real signal — but it is **not** a stranger taking an action. Have the user
  **keep the comparative read** (variation A beats B, this claim triggers pushback) and take the go/no-go
  from real behavior — a live post's actual engagement, a smoke test, a pre-sale. Route the real test to
  `/sales-idea-validation` and `/sales-funnel`.
- **Use it for relative comparison, not absolute truth.** Its strength is **ranking variations against
  each other**; its weakness is **false positives** on absolute scores (the founders' own example: a
  non-working service scored 81, a working one 88 — both "high"). Tell the user to trust "B > A" more than
  "88/100 = good," pick the winning variation, then validate it for real.
- **Match the product to the job.** The **self-serve** tool (a tiny **Free** tier — 3 credits + a 2-week
  trial — then **Pro ~$40/mo** unlimited; flag prices as best-effort to confirm on societies.io) tests
  *content and messaging* against a LinkedIn-style audience — perfect for a solo founder pre-testing a post.
  **Radiant** (enterprise, contact-sales) is for **data-grounded studies** on first-party CRM/research
  data with follow-up interviews — overkill for a solo maker with no data to ground it in. Don't send a
  solopreneur to the enterprise tier.
- **Define the audience as tightly as you can.** Accuracy depends on the society matching your real
  audience — use demographic (age/gender/location/income/education) and professional (company/industry/job
  title) targeting, or first-party data on enterprise. A generic society gives a generic prediction; a
  precisely-specified one is the whole point. Flag when the user's audience is too vague to simulate well.
- **There's no public API — don't plan an integration around it.** If asked to automate, batch, or pipe
  results elsewhere, say plainly there's **no documented public API, no webhooks, no Zapier/Make, no MCP**
  (the `docs.societies.io` subdomain doesn't resolve). The self-serve product is browser-only; enterprise
  Radiant ingests first-party data but exposes **no public developer API**. The thing worth automating is
  the real signal (published-post engagement, landing-page conversions), not the simulated score.

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) — Societies' products, pricing, and accuracy claims move; verify at societies.io.*

- **A synthetic engagement score is not demand.** The founders say synthetic audiences "should never
  replace listening to real people." Use it to pre-test messaging; earn the go/no-go from real behavior.
- **False positives are the failure mode.** A bad message can still score high (81 vs 88 in the founders'
  own example) — trust *relative* comparisons between variations far more than any absolute number.
- **Accuracy claims are vendor self-reported.** R²=0.78 (LinkedIn engagement), 95% opinion-distribution
  accuracy, 90% persona coherence — none independently audited. Treat as marketing until verified.
- **Personas skew to public/LinkedIn data.** The R²=0.78 fit is for **LinkedIn** engagement specifically;
  generalizing to other channels or niche/offline audiences is weaker (the founders flag this).
- **Two products, very different access.** Self-serve (societies.io) = $40/mo, content/message testing.
  Radiant = enterprise, contact-sales, first-party-data societies. Don't quote enterprise capabilities to
  a self-serve user or vice versa.
- **No public API, no webhooks, no Zapier/Make/MCP.** It's UI-only; there's no supported way to script,
  batch, or export programmatically. `docs.societies.io` does not resolve.
- **Free tier is tiny.** 3 credits + a 2-week trial, then Pro at $40/mo for unlimited simulations —
  confirm current terms on societies.io before relying on them.

## Related skills

- `/sales-idea-validation` — The tool-agnostic validate-before-building method + the full synthetic-research and validator landscape (use this to decide build-or-not and to compare Societies against iMario/Synthetic Users/Delve; a synthetic score is not demand)
- `/sales-syntheticusers` — Synthetic Users platform help (the pure-play synthetic **interview-study** tool — multi-participant AI interviews with a real public REST API; contrast its independent-participant studies against Societies' influence-network simulation)
- `/sales-imario` — iMario platform help (reusable **Synthetic Individuals** with persistent memory across six jobs, Pro-gated API; the reusable-persona cousin to Societies' one-shot network runs)
- `/sales-delve` — Delve AI platform help (analytics-connected **data-grounded personas** + Digital Twins you chat with; contrast a single chattable persona against Societies' whole-audience network)
- `/sales-funnel` — Build the smoke-test / fake-door landing page that measures the real demand a synthetic score only predicts
- `/sales-social-media-management` — Actually publish and schedule the content once Societies has helped you pick the winning variation
- `/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: "Societies scored my LinkedIn post 88 — should I post it?"
**User says**: "I ran my launch announcement through Artificial Societies and it scored 88. Is that good enough to publish?"
**Skill does**: Explains the score is a **synthetic prediction**, useful for comparison but prone to **false positives** on absolute numbers (the founders' own example: a non-working service scored 81, a working one 88 — both "high"). Advises running **two or three variations** and trusting "B beats A" over the raw 88, then picking the winner. Notes the R²=0.78 LinkedIn fit means the *relative* read is trustworthy, but real engagement on the live post is the actual signal — and if this is really a build-or-not question, routes to `/sales-idea-validation`.
**Result**: The user pre-tests variations, ships the winner, and reads the real post's engagement as the truth.

### Example 2: Free vs Pro vs enterprise Radiant
**User says**: "I'm a solo founder. Do I need the enterprise version of Societies, and what does it cost?"
**Skill does**: Maps the tiers — **Free (3 credits + 2-week trial)** → **Pro ($40/mo, unlimited simulations)** for self-serve content/message testing → **Team** → **enterprise Radiant** (contact-sales, first-party-data societies with follow-up interviews). Explains Radiant is for **data-grounded studies** a solo maker has no data to ground yet, so **Pro is the right tier** for pre-testing posts. Flags prices as best-effort to confirm on societies.io.
**Result**: The user picks Pro and skips an enterprise sales call they don't need.

### Example 3: Can I automate Societies via API? (developer/automation)
**User says**: "I want to run posts through Societies from a script and pull the scores into my own dashboard."
**Skill does**: States plainly there's **no documented public API, no webhooks, no Zapier/Make, no MCP** — the `docs.societies.io` subdomain doesn't resolve and the self-serve product is browser-only; enterprise Radiant ingests first-party data but exposes no public developer API. Suggests that if a pipeline is required, the thing worth automating is the **real** signal — published-post engagement (a social API) or landing-page conversions — not the simulated score, and points to `/sales-social-media-management` and `/sales-funnel`.
**Result**: The user avoids building on a non-existent API and automates the real signal instead.

## Troubleshooting

### The score feels high even for a message I know is weak
**Symptom**: Societies returns an encouraging engagement score for content that flopped or feels off.
**Cause**: Synthetic personas modeled from public data over-predict plausible-sounding content; absolute
scores carry **false positives** (the founders' own 81-vs-88 example).
**Solution**: Stop reading the absolute number as a verdict. Run **multiple variations** and use Societies
for the **relative** comparison (which variation wins), then validate the winner with real behavior — a
live post's engagement or a smoke test via `/sales-idea-validation` and `/sales-funnel`.

### The prediction doesn't match my real audience
**Symptom**: Reactions don't look like how your actual audience responds.
**Cause**: The society is too **generic** or the R²=0.78 fit is LinkedIn-specific and you're testing a
different channel/niche.
**Solution**: Tighten the audience definition (demographics + professional targeting, or first-party data
on enterprise) so the society matches your real audience; treat non-LinkedIn or niche/offline predictions
as weaker, and always confirm against a small real test.

### I want an API or a way to export/automate
**Symptom**: Want to script Societies or pull scores into another system.
**Cause**: Societies has **no documented public API, webhooks, or iPaaS connectors**; `docs.societies.io`
does not resolve.
**Solution**: There's no supported programmatic path — the self-serve product is used in the browser.
Automate the **real** signal instead (published-post engagement, landing-page analytics), and see
`/sales-idea-validation` and `/sales-funnel`.