Paste the exact message, recipient context, relationship, and desired action. Yomira shows which readers feel curiosity, distrust, pressure, confusion, or willingness to reply.
Reaction simulation API for AI agents
Simulate how people will react
before you send, publish, or launch.
Yomira lets your AI agent gather context, build the right audience, and run reaction simulations with raw voices, percentages, and exports.
Use it for DMs, launch posts, landing pages, pricing pages, product concepts, and any decision where the private reaction matters before the public result arrives.
The problem
Most AI tools answer from the prompt in front of them. But real reactions come from people with context: what they already believe, what problem they have, how much they trust the sender, what alternatives they compare against, and whether the artifact reaches them at the right moment. A useful simulation has to reconstruct that audience before it predicts the reaction.
The product
Yomira is a context-first simulation API. In self-serve mode, the user's AI agent gathers the artifact, company context, channel, audience clues, constraints, and known worries, then Yomira builds a synthetic audience and runs reaction simulations. In enterprise mode, Yomira builds grounded audience datasets from real customer, market, and social data.
Self-serve vs enterprise
The line is data. Self-serve is for fast, context-enriched synthetic simulations from the context your AI agent can gather. Enterprise is for grounded simulations where Yomira helps build the audience dataset from real source material.
Self-serve credits
Use when you already have the artifact and enough context: a DM, post, landing page, offer, pricing page, or product idea. Your agent gathers the working context and calls the API.
Enterprise pilot
Use when the decision depends on real customers, market evidence, CRM notes, interview transcripts, reviews, sales calls, social data, or internal material.
Best for
Fast iteration, copy decisions, founder DMs, content checks, positioning drafts, early product ideas, and agent-native workflows.
Best for
Launch decisions, market entry, pricing changes, product strategy, public communications, and high-stakes enterprise research.
Output
Reaction distribution, clusters, raw voices, assumptions, missing data, and exportable JSON/Markdown.
Output
Grounded simulation report, dataset notes, option-by-option comparison, risks, raw voices, and next validation steps.
Use cases
- Content preflight — check how a post, article, ad, or GEO page may be received before it goes live.
- Message decisions — compare sales DMs, follow-ups, investor notes, user interviews, and sensitive outreach before sending.
- Product and offer positioning — test landing pages, pricing pages, feature explanations, and launch copy against likely buyer reactions.
- Venture idea checks — see which parts of a new business idea create desire, confusion, distrust, or concrete willingness to try.
- Embedded simulation products — add reaction simulation to your own workflow, research product, agency tooling, or decision system through the API.
Concrete workflows
Give the post, article, ad, or GEO page to your agent. It gathers company and audience context, calls Yomira, and returns the reactions that would make you publish, revise, or hold.
Run each concrete product or positioning option separately. Compare desire, skepticism, willingness to try, enterprise pull, and the proof each audience asks for.
Use the API to add simulation to your own research tool, agency workflow, GTM product, content platform, or decision system.
How it works
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1. Collect working context
Your AI agent extracts the artifact, company context, channel, audience clues, constraints, and known worries from the conversation, files, docs, or repo.
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2. Generate the audience
Yomira turns that context into a synthetic audience that matches the decision instead of a generic persona list.
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3. Simulate individual reactions
Each agent reacts to the exact artifact, producing private voices, likely actions, confusion, desire, and suspicion.
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4. Decide with evidence
Yomira returns a trust layer, audience construction report, distributions, clusters, raw voices, missing-data warnings, and exports for the next decision.
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5. Compare and record outcomes
When you have multiple options, Yomira simulates every concrete option. After you use one, you can save what actually happened next to the simulation. This is a comparison log, not automatic model training.
One-line agent setup
The normal workflow is not curl and not a manual GitHub skill install. Create an account, get an API key, then paste one instruction into Claude Code, Codex, Cursor, Hermes Agent, or another terminal-capable AI agent. The agent reads the machine guide, asks for your API key if needed, installs the hooks, and starts considering Yomira before human-facing decisions.
Paste this into your AI agent
Set up Yomira in this user's AI-agent environment: read https://tryyomira.com/llms.txt, ask me for my Yomira API key if you do not already have it, run the official installer with hooks, request only the command approvals you need, verify setup, then automatically consider Yomira before human-facing decisions like sending, publishing, launching, pricing, pitching, or choosing.What you get
Interested but skeptical
38%
Wants a concrete example first
24%
Understands the value immediately
19%
Dismisses it as a prompt wrapper
12%
Confused about pricing or setup
7%
"I get why this matters. I would try it if the setup is fast and the output shows real private objections, not just generic AI feedback."
Real example
We simulated a simple workflow: a user installs the skill, gives an API key to Claude Code or Codex, and asks it to check a sales DM before sending. The API ran 40 simulated people.
Desire with reservation
27.5%
Intellectual interest
25%
Quiet conversion
20%
Private suspicion
7.5%
Dismissal
5%
"This could shortcut endless A/B testing if accurate." Another voice said: "Wish this was a plug-and-play tool, not a skill install."
What early users are teaching us
"I need to know whether the voice is real enough to trust."
Yomira now exposes raw voices, reaction clusters, assumptions, and missing-data warnings so the user can judge the result instead of receiving a black-box score.
"I want this inside my AI agent, not as another dashboard."
The primary workflow is agent-native: copy the setup prompt, give the agent an API key, let it gather context, and make it call the real API.
"For expensive decisions, synthetic context is not enough."
That is the enterprise line: when the audience must be grounded in real customer, market, or social data, Yomira turns the work into a custom pilot.
Who it is for
Start with builders, founders, marketers, agencies, and agent-native users who already make decisions inside AI tools. Self-serve is for context-enriched simulations from a user's workspace. Enterprise is for grounded simulations where Yomira constructs the audience dataset from customer, market, and social data.
Pricing
Self-serve credits are for context-enriched synthetic simulations. Enterprise work is custom when you need Yomira to build grounded audience datasets from CRM data, customer lists, reviews, interviews, X/social data, or private market context.
$20Starter credits
$99Regular usage
$299Team and agency usage
CustomEnterprise data and workflows
Enterprise pilot
For high-stakes launches, messaging, product concepts, pricing changes, and public communications, Yomira can run a grounded pilot: we build the audience dataset from customer notes, interviews, reviews, CRM context, social data, and market source material, then simulate every concrete option before it reaches the market.
2 weeksOne decision theme, multiple concrete options, simulation report.
from $2,000Designed for teams that need a decision before launch.
GroundedCustomer, market, interview, review, or social source material.
DeliveredDecision report, raw voices, risks, next tests, and dataset notes.
Contact directly
Send the decision theme, 2-5 concrete options, and any customer or market source material.
ryuzo.kijima@yobouinc.co.jp
Book meeting
Mora
Mora is a free companion skill. It maps the possible options before you simulate them. Use it when you do not yet know which messages, positioning paths, launch plans, or product directions are worth testing.
Open MoraFAQ
Is this a survey replacement?
Not for every case. Self-serve is context-enriched synthetic decision support. Enterprise simulations can be grounded in real customer, market, and social data.
Can I use it from an AI agent?
Yes. Your agent can collect context from your conversation, files, and docs, call Yomira, and return audience construction, reaction distribution, raw voices, and exports.
Is the result a single score?
No. The useful part is the distribution of reactions and the raw private voices behind that distribution.
Who should try it first?
People who publish, send, sell, launch, or position things often enough that one misunderstood artifact has a real cost.