Simulation: Salary Negotiation

There is no real risk to your actual salary or job -- the conversation is entirely simulated, and no number agreed upon here affects your real pay (see the transparency question below). And unlike role-play with a friend, the simulated negotiating counterpart follows a pre-documented flexibility and negotiation range instead of free improvisation, and your negotiation process is scored by an independent evaluator citing evidence from your own words.

This product differs from the rest of the family on one key point: the final negotiated number itself is never judged by free-form AI at all -- it is extracted by a deterministic parser (fixed code, not a language model) and compared mathematically against a pre-set agreement zone for each difficulty level, so there is no subjective judgment on "whether your number is good." Your process (clarity, listening, framing, integrity) is scored by an independent evaluator instead, and its accuracy is measured against two standards before any real session opens: self-consistency on the same transcript, and agreement with a human expert on a calibration set of at least thirty sessions. This calibration is mandatory and has not been completed yet.

Anyone preparing to negotiate a salary (an annual raise, a new job offer, a contract renewal) who wants to practice stating a clear number, listening to the other side's constraints without abandoning their position too quickly, and building their case around real value rather than need alone.

Between 6 and 40 conversational turns depending on the difficulty level you choose -- most sessions in practice take between 10 and 20 minutes.

You get a debrief with exactly 3 to 6 indicators, each with a score (0 to 3) and a literal quote from your own words explaining exactly why that score was given, plus the final negotiated number you reached and where it falls within the agreement zone. One thing that worked and one specific next step to work on -- not a final verdict on your skill, a practical recommendation to build on in your next attempt or your upcoming real negotiation.

Yes. In training mode you can retry the same scenario to learn; in test mode an equivalent (not identical) scenario version is used, to prevent memorizing a saved counter-offer instead of developing real negotiation skill. Your result may differ because the conversation is genuinely live -- how you make your ask and listen drives how the other side's offers move each time, not a pre-fixed script.

Fully artificial intelligence. The negotiating counterpart is a simulated persona with a pre-documented trait profile and negotiation range -- there is no real human behind the screen at any point in the session, and no real effect on your actual salary or job offer no matter what the session's outcome is.

By this product's design, your session text is used exclusively to generate your evaluation (debrief) and to make it available to you later for review (replay) -- it is not shared with any third party outside the platform, and not with your real employer in any form. This is an approved design commitment that will be enforced when the runtime engines are built, and it will be live-verified before real sessions open to users.

Every score in your process evaluation is tied to a literal quote from your own session text -- you can always review the quote and check it yourself. And the final negotiated number is extracted programmatically from your actual session text, not guessed -- so you can verify it yourself too. If you believe a quote is inaccurate or a score doesn't reflect what you actually said, your evaluation is not a final closed verdict -- the product is designed to carry an appeal path, and its details will be published once real sessions open.

No. This session produces a scorecard for one scenario only -- process scores, the final negotiated number and its place within the agreement zone, and a pass/not-pass decision against that scenario's specific threshold. A verifiable interaction license (a credential you can check via a code/link) requires accumulating multiple qualifying sessions across multiple products within the separate Interaction Licenses family -- it is not granted directly from a single session here.