Four ways to get an answer, honestly compared.
userken against asking a general purpose model, against traditional research, and against the enterprise synthetic audience category. No competitor names, no borrowed numbers.
Where each one wins.
| userken | General purpose LLM | Traditional research | Enterprise synthetic audience platforms | |
|---|---|---|---|---|
| Time to an answer | Seconds to minutes | Seconds | Weeks, including recruitment and fielding | Minutes once you are onboarded, months to get there |
| What the answer is grounded in | A named corpus of public reviews, with the review ids recorded | Training data you cannot inspect | Responses from real recruited people | Usually your own first party data, plus licensed panels |
| Needs your own data to start | No | No | No, but it needs a budget and a sample | Typically yes, that is the onboarding |
| Can you build an audience for a competitor | Yes, because the source data is public | It will produce something, grounded in nothing specific | Hard and expensive, you must recruit their users | Usually not, the data is yours |
| Segment breakdown | Weighted personas with per segment distributions | One voice, no weights | Yes, if the sample was designed for it | Yes, that is the core of the category |
| Published accuracy method | Yes, metrics, hold outs and baselines on a public page | No | The methodology is the deliverable | Often a validation claim or white paper, methods vary |
| Statistically representative of a population | No, reviewers are self selected and we say so | No | Yes, when the sample is designed and weighted for it | Depends entirely on the underlying panel |
| Good for regulated or published claims | No | No | Yes | Generally positioned for internal decisions, not substantiation |
| Works inside your AI assistant | Yes, MCP native, same engines as the web app | It is the assistant | No | Rarely, these are dashboards |
| Self serve | Yes | Yes | No | No, sales led |
| Cost shape | Free tier, then per seat with metered credits | A subscription you already pay for | Per project and per respondent, the respondents dominate | Annual contract, typically five figures and up |
| Best used for | Narrowing options fast, finding segments, pre-testing wording, competitor audiences | Drafting the question, not answering it | Decisions that must hold up to scrutiny | Large organisations running continuous insight programmes on their own data |
Written to be useful rather than flattering. We do not name other vendors or quote their numbers, because their pricing and their validation results are theirs to publish. Where a row says traditional research wins, it wins.
Use the right tool.
Use a general purpose model
To draft the question, to summarise what you already have, and to think out loud. Not to produce a distribution you will put in a deck.
Use userken
To narrow a field of options in minutes, to find the segments that exist in a category, to pre-test wording, and to interview an audience you do not own.
Use traditional research
When the number will be published, when it is regulated, when it must be representative, and when being wrong is expensive. We will say so.
Use an enterprise platform
When you have a large first party dataset, a continuous insight programme, and the procurement appetite for an annual contract and an onboarding project.
Try the self serve option.
Public audiences, published method, and it works inside Claude. Request access and bring a real question.