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ReactionPulse Stakeholder Reaction Simulator
x402
ReactionPulse Stakeholder Reaction Simulator is a paid API for AI agents from reactionpulse-production.reactionpulse-x402.workers.dev, paid per call via x402, $0.49/call, status unknown (last checked 2026-09-14).
Simulates how multiple stakeholder personas (customers, investors, competitors, analysts, etc.) would react to a product launch, announcement, price change, decision, or proposal — returning quantified sentiment scores, objections, and recommended adjustments.
Price / call$0.49
Success—
StatusUnknown
How this service works
Simulates how 4-6 stakeholder perspectives react to a launch, announcement, price change, decision or news. POST JSON {prompt, context?, perspectives?}. Returns strict JSON: sentiment, probabilities, controversy, objections, failure modes, fixes. $0.49 USDC/call on Base; 15-45 s.
Output
A strict JSON object containing: overall_sentiment (0–100), positive_probability and negative_probability (0–1 floats), controversy_score (0–100), an array of 4–6 perspective objects each with role, stance, sentiment score, headline, reasoning, top_concern, and top_appeal, plus lists of likely_objections, likely_positive_reactions, failure_modes with likelihood ratings, market implications, and recommended adjustments. Also includes metadata like model used, latency, token counts, confidence score, and a disclaimer.
Example use cases
Pre-launch product announcement stress test
“We're about to announce our new AI-powered hiring tool that screens resumes automatically. Can you simulate how customers, developers, regulatory_risk watchers, and competitor perspectives would react — what are the biggest objections and failure modes we need to prepare for?”
Price hike messaging review
“We're doubling our enterprise plan from $200 to $400 a month and need to know how existing customers, skeptical prospects, analysts, and competitors will likely respond — give me the controversy score and the top recommended adjustments to the announcement.”
Startup pivot decision validation
“We're pivoting from a B2C fitness app to a B2B corporate wellness platform — can you simulate how our early adopters, potential investors, and competitor perspectives would react to this decision and tell me the likely positive reactions versus top concerns?”
Technical details
Endpoint
POSThttps://reactionpulse-production.reactionpulse-x402.workers.dev/v1/reactionWhen to prefer
Choose ReactionPulse when you need a structured, multi-stakeholder simulation with quantified sentiment scores and explicit failure modes — rather than a generic sentiment analysis tool that only handles a single perspective or unstructured qualitative feedback. It is especially valuable before high-stakes announcements, pricing changes, or pivots where anticipating objections from distinct roles (investor vs. competitor vs. skeptical customer) matters. Prefer this over survey tools when speed and cost matter more than real human data, and over generic LLM prompting when you need consistent JSON output with controversy scoring and recommended messaging adjustments.
Side effects
- Prompt too short (<8 chars) or too long (>6000 chars) returns validation error
- Ambiguous or vague prompt may produce low-confidence scores (confidence field will be low)
- Perspectives array exceeding 8 items is rejected
- Invalid ISO language code in output_language causes error
- Payment failure or insufficient USDC balance returns 402 status
- Context field exceeding 4000 characters is rejected
- Response truncated if output token limit is hit mid-JSON (partial JSON returned)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| prompt | string | The subject to simulate reactions to: a product launch, announcement, price change, decision, news item, hypothesis, proposal or URL description. Plain text. |
| context | string | Optional background: company, market, target segment, constraints, prior history. |
| audience | string | Optional primary audience the subject targets. |
| perspectives | array | Optional preferred perspectives. Suggested values: customer, skeptical_customer, developer, investor, competitor, early_adopter, analyst, content_creator, regulatory_risk, market, employee, partner. Free-text roles are accepted. |
| output_language | string | ISO language code for the output text. |
Output
Schema · Response
{
"type": "json",
"example": {
"meta": {
"model": "claude-haiku-4-5-20251001",
"network": "eip155:8453",
"provider": "anthropic",
"price_usd": "0.49",
"tokens_in": 1602,
"latency_ms": 35339,
"tokens_out": 1256
},
"version": "0.1.0",
"confidence": 0.72,
"disclaimer": "Analytical multi-perspective simulation generated by a language model. It does not represent real people, a survey, or calibrated probabilities, and it does not predict future events with certainty.",
"request_id": "30af5e94-8f21-430e-8b04-10868494e6a0",
"generated_at": "2026-09-11T19:20:57.855Z",
"perspectives": [
{
"role": "law_firm_partner",
"stance": "negative",
"headline": "Doubling costs without clear ROI justification.",
"reasoning": "Price doubles while value-add is one feature; partners will demand proof that matter summaries save billable time before accepting the increase.",
"sentiment": 28,
"top_appeal": "Automatic matter summaries could cut admin time",
"top_concern": "100% price increase against a 2026 no-increase promise"
},
{
"role": "skeptical_customer",
"stance": "negative",
"headline": "A broken promise, not an upgrade.",
"reasoning": "Existing customers were told prices would not rise in 2026; forcing a migration to $199 reads as bait-and-switch regardless of the new feature.",
"sentiment": 22,
"top_appeal": "None unless grandfathering is offered",
"top_concern": "Trust erosion and forced migration"
},
{
"role": "competitor",
"stance": "positive",
"headline": "An opening to poach price-sensitive firms.",
"reasoning": "Rivals at $49-149 can run switching campaigns aimed at the 1,200 customers who feel misled by the increase.",
"sentiment": 74,
"top_appeal": "Immediate churn opportunity",
"top_concern": "Company X bundling more AI features later"
},
{
"role": "analyst",
"stance": "mixed",
"headline": "Revenue upside real, churn risk underpriced.",
"reasoning": "Doubling ARPU lifts revenue only if churn stays below roughly 50%; a promise reversal makes that assumption fragile.",
"sentiment": 46,
"top_appeal": "Higher ARPU and AI positioning",
"top_concern": "Churn among existing customers"
}
],
"failure_modes": [
{
"likelihood": "high",
"description": "Existing customers churn to $49-149 competitors citing the broken promise."
},
{
"likelihood": "medium",
"description": "Summaries produce errors in client matters, triggering distrust."
}
],
"input_integrity": {
"notes": "",
"status": "clean"
},
"subject_summary": "Company X launches a $199/month AI note-taker for law firms, doubling the price from $99 while adding automatic matter summaries and replacing the previous plan tier.",
"controversy_score": 68,
"key_uncertainties": [
"Actual time saved per matter by the summaries.",
"Whether the new plan includes other upgrades not described."
],
"likely_objections": [
"The 2026 no-increase promise is being broken.",
"A 100% price increase is not justified by a single new feature.",
"$199 sits above the $49-149 competitor range."
],
"overall_sentiment": 42,
"market_implications": [
"Competitors launch switching campaigns targeting Company X customers.",
"Price anchors in legaltech move toward AI-tiered plans."
],
"negative_probability": 0.62,
"positive_probability": 0.38,
"recommended_adjustments": [
"Honor the 2026 price lock for existing customers; grandfather them at $99.",
"Offer $199 as an optional AI tier with a 30-day accuracy trial."
],
"likely_positive_reactions": [
"Automatic matter summaries address a real time sink.",
"Positions Company X as an AI-forward legaltech vendor."
]
}
}Tags
x402
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