sales-syntheticusers

name	sales-syntheticusers
description	Synthetic Users (syntheticusers.com) platform help — run AI-moderated research studies (problem-exploration and concept-testing interviews) against synthetic participants instead of recruiting real people, grounded in your own data via RAG, with insights reports, saturation scoring, and a full REST API (Bearer auth, Python + TypeScript SDKs, SSE streaming, PDF export). Use when setting up a Synthetic Users study or audience, interpreting a synthetic interview report, grounding participants in your own customer data, pulling study transcripts or summaries via the API, wiring the create-study to interview to summary flow into a pipeline, or judging whether a synthetic 'yes' is real demand. Do NOT use for comparing idea-validation or persona 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 Synthetic Users]
license	MIT
version	1.0.0
tags	["sales","pre-launch","platform"]
github	https://github.com/SyntheticUsers

Synthetic Users Platform Help

Synthetic Users (syntheticusers.com) is an AI synthetic user-research platform: you define an audience, write a topic guide, and run interviews against AI synthetic participants — multi-agent personas with stable OCEAN personality profiles — instead of recruiting real respondents. Three interview types: problem exploration, concept testing, and custom script. You can ground studies in your own data via RAG (customer interviews, support tickets, segment definitions), and it returns an insights report (themes, executive summary, verbatim quotes) with saturation scoring to tell you when you've heard enough. Unusually for this cluster it ships a full REST API (Bearer auth, Python + TypeScript SDKs, SSE streaming, PDF export).

Its signal is directional, not demand: synthetic participants model existing/public/training data, so they skew agreeable, generic, and Western-biased and cannot surface a genuinely new unmet need. Treat any synthetic result as a hypothesis to disconfirm with real behavior, not 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 Synthetic Users?
   • A) Set up a study — pick an interview type (problem exploration / concept testing / custom script)
   • B) Define or generate an audience of synthetic participants
   • C) Ground a study in your own data (RAG upload) for more specific answers
   • D) Interpret an insights report / transcripts / saturation score you already ran
   • E) Automate it — pull studies/summaries/transcripts via the API, or wire the flow into a pipeline
   • F) Decide whether to trust it — is this a valid stand-in for real research?
2. What's the research question and who's the audience? A sharp audience + topic guide yields a usable result; 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