Centercode vs. Synthetic-only

Most of the tools we read say a survey, interview round, focus group, or concept test comes back quickly, and because the participants are generated there is nobody to invite or wait for. Access differs: some can be started on your own with a free trial or monthly credits, others are sold only through a demo, a request form, or a proposal, and one sells yearly plans only.

Some of the tools we read let you repeat a study to watch change over time. One says you can schedule a study to rerun daily, weekly, monthly, or quarterly, and the page that says so names no plan. Others describe repeat rounds of the same questions, a quarterly tracker in a customer story, generated participants that keep their memory across sessions, or a weekly routine of questions put to saved audiences that the team runs itself. One lists tracking over time under its enterprise and custom tier, through its sales team. We found no built-in program with dated phases, events along the way, gates people must clear first, or a way of trying a product with real people before launch, and some of these tools say on their own blogs that rounds run with real people remain the reference record.

We found no standing group of real people who join, post, and discuss themes over time in the tools we read, and one of them says on a comparison page that real advisory boards remain the benchmark for building executive relationships. The closest we found: most of the tools we read let you keep a generated audience and ask it again whenever you like. One calls its platform an always-on audience, on a license priced by the audiences built and the seats. One sells persistent, reusable cohorts as a seat-based subscription, though its pricing page lists per-study prices and says there are no subscriptions. Others keep saved audiences you reuse across studies, with limits that rise by plan where they publish them, and some describe advisory boards made of generated participants.

Some of the tools we read start you from ready-made templates for common research goals. One has a library of survey templates for goals such as customer satisfaction, product-market fit, and pricing, one says it has test templates for concept, message, and pricing studies, another says it has pre-built templates, one has a set of named research methods built in, one publishes how-to guides for common goals, and one groups its managed work into modules such as segmentation, positioning, and creative. We found no templates for trying a product before launch, advisory boards, ambassador programs, or other kinds of program in the tools we read. The pricing pages we read do not name templates as a feature of one plan, and one lists a pre-built library for the whole platform.

We found no ready-made program in the tools we read for AI red teaming, ambassadors or advocates, bug bounties, or acceptance testing. One says on its own blog that it is not an acceptance testing tool, that final acceptance checks stay with people, and that trying a product with real users stays human. The closest we found are blog posts about using generated participants to rehearse a product trial, or to criticize a draft message before real people see it, which one post calls a red team test, and studies that test how an audience might react to a possible brand ambassador.

We found no self-serve way in the tools we read to invite your own customers or community to take part, and some of these tools say plainly that their participants are not real people. The closest we found: most of the tools we read say you can bring your own customer records, past survey results, interview transcripts, or documents so the generated participants are built from them, which some offer only on paid or higher plans or after a custom quote.

We found no network of real people that you can draw on yourself in the tools we read. The closest we found: several of the tools we read offer ready-made audiences of generated participants that you can pick without building one, some call these panels, and some let you try them with a free trial or a free account. Some say these audiences are built from interviews, surveys, or public records of real people, and one says plainly that it keeps no fixed group of recruited people.

We found no applications or screening of real people in the tools we read, and one says plainly that no recruitment or screener is needed because the participants are generated. The closest we found: most of the tools we read let you describe the audience you want by traits such as age, job, place, or behavior, and some let you set how many generated participants fall in each group. Some limit how many audiences you can build or keep by plan, and some set up custom audiences through their sales team.

We found no community of real people that you keep, and no space for members to join, in the tools we read. The closest we found: most of the tools we read let you save an audience of generated participants and use it again in later studies, and some limit how many you can keep by plan. That is a saved set of generated participants, not people who belong to your program.

Most of the tools we read say results arrive quickly, often within minutes or hours, and that nobody has to be found or matched first because the participants are generated. Some say this holds for senior or niche roles that take a long time to recruit. Some say audiences built from your own data, or built for you by their team, take longer, and some sell this only through a demo or a quote. Some offer a free trial or a free account, and one sells yearly plans only.

Most of the tools we read say they can generate participants from many countries and regions, and some name their markets. Fewer say anything about languages: some say they support several, and one says its models support English-language surveys only. One gives different country counts on different pages, so we leave the figures out. We found no plan limits on countries or languages.

We found no product page for a study where people record their camera or screen in the tools we read. The closest we found: some of the tools we read say generated participants can navigate a page or prototype and say what confuses them, one adds a replayable session with notes on what they were thinking, and one offers generated video clips of a persona, with limits that depend on the plan or on credits. Some of the tools say watching people use a product calls for real participants.

Some of the tools we read say you can ask a generated participant, or a small generated group, your own questions and follow-ups as the conversation goes, with nothing to book, and some add a real-time voice call. We found no booking, reminders, or observers for a session with a person. Where limits are published they depend on the plan or on credits.

Most of the tools we read say they run interviews with generated participants and follow up on what those participants say, and some say an AI interviewer works through your discussion guide and returns transcripts, themes, and summaries. They say results arrive quickly with no recruiting or scheduling. The answers come from generated participants, not from people who took part. Limits depend on the plan: one is annual plans only, some draw each interview from a pool of credits, one prices by the study and does not list interviews on its pricing page, and one offers interviews only as a managed engagement it scopes after a demo.

Some of the tools we read say they run trade-off studies with generated respondents, where choices between product options show how much each feature and price sways the pick, and some of those add a what-if simulator for market share. Plan details differ: one says every plan includes its study types while its own method catalog says availability can depend on the plan, one says to reach out for access and does not list it on its pricing page, one gives a prompt template to run it yourself, and others are sold through a demo or a license. One says its generated price results show a direction rather than a firm answer.

We found no way for participants to file issues, ideas, or praise as they use a product, in the tools we read. The closest are studies where some say generated participants can be pointed at a page, screenshots, or a prototype and say where they hesitate, which depend on the plan or use credits. One tool describes a chat widget you can add to your own website, set up with the generated audiences you pick and available depending on the plan and on which integrations are enabled, and we found no page saying it collects issues, ideas, or praise.

We found no rewards or payments to participants that a customer can set up in the tools we read. Some of them say their approach needs no incentives or payments to participants at all.

We found no reminders, thank-yous, or other messages to participants, and no way to schedule them, in the tools we read. Some of them say their studies need no recruiting or scheduling, and the closest we found is finished results pushed to your team’s chat tools.

In the studies the tools we read describe selling, a model generates every participant. Most of the tools we read say their generated participants are built from, or checked against, information about real people, such as survey results you bring to them, and most say real people are still needed to confirm important decisions. One customer story began with a real survey run through a partner that supplies respondents, before generated participants were built from it. Where plans are stated, building participants from your own data is a top-plan, custom, or quoted item in some tools and part of ordinary paid plans in others.

The tools we read are built around generated participants who answer surveys, interviews, and group discussions, or who are simulated as whole populations. They say results come back quickly with no one to recruit, and several describe the work as early exploring that points a direction. Some go further than a quick test: they say you can question a saved audience again and again, reach groups that are hard to recruit, and ask questions that would be commercially sensitive to put to real customers. Pricing varies: monthly plans or credits (some with a free trial), annual plans, a license, a price per study, or a demo, quote, or request form first.

Some of the tools we read offer digital twins, meaning stand-ins for real people, and say they build them from real people’s survey answers, interviews, or records: a twin for each person in some tools, a twin for a whole audience in others. Another uses the word for a stand-in made from a description of an audience. Some of the tools hold back from the word for their own participants, one because it models traits and context rather than copying single people, and another because it says the word can suggest a more complete copy of a person than its approach supports. Some say the data behind their models is anonymized, while one says its cloning feature can capture personal details and should be used with caution. Where plans are stated, twins come with a seat license, a monthly limit on audiences you clone from your own data, or a monthly plan; others start with a demo or request form.

Most of the tools we read say you can bring in your own documents or data, such as past interviews, survey files, and support tickets, but mainly to shape their generated participants. One also says it can analyze feedback files you upload, and one pulls in tickets and cloud files from connected tools as research material, which its guides say it only reads. We found no page describing a scan of public pages or forums that you point at what customers say. One tool’s pages say it does not watch social media, and others use web research to build their participants or check their reports. One tool’s page says it turns indirect signals such as support tickets, reviews, and forum discussions into insights without saying how it reads them, and one publishes automation templates, run in your own automation tool, that pull in public video comments, a social account’s posts, or news feeds. On plans, one pricing page lists basic data analysis on each plan but bringing in past studies and documents only as a custom-quoted add-on on its larger plan, and another tool’s guides say its imports from connected tools depend on the plan and workspace settings. One lists self-serve uploads from its second plan up, and one lists audiences built from your data on its largest plan only.

Most of the tools we read say they write summaries of what their generated participants said: themes, key quotes, executive summaries, and recommended next steps, quickly. One says its summaries start with a plain-language overview and rank themes by how many participants raised them, and one says it can also summarize interview recordings and transcripts you upload. On plans, one tool’s pricing page includes an executive summary in every study, another lists its research report on each paid plan, and a third says the study types and features you see depend on your workspace and plan. We found no plan named for summaries on the other pages we read.

Several of the tools we read say you can export or share transcripts of the generated sessions, as files or inside reports with quotes, and one gives speaker-attributed transcripts when a session ends. One also lets you highlight passages in the generated interviews and export those highlights. We found no highlight clips cut from recordings of people, since the participants are generated; one lists a monthly allowance of generated persona videos on each paid plan, and another lays generated reactions against moments in a video you upload, which it lists from its second plan up. On plans, one pricing page lists exports on each of its self-serve plans and another lists white-label reports, which carry your own branding, on its larger plan only; we found no plan named for transcripts on the other pages we read.

Some of the tools we read say they group the answers their generated participants give into themes and show how many raised each, and one says equivalent wording shares a category while opposing claims stay separate. That grouping sits inside a study. One tool says its research assistant can pull findings from several studies into one report, and one keeps findings in a searchable library, but we found no step that merges matching feedback from different sources into one insight with a running body of evidence. The closest is one tool’s analysis of uploaded files, which it says categorizes responses from outside sources and finds recurring patterns. One lists the assistant that combines studies from its second plan up. The other pages we read name no plan for any of this.

One tool says its summaries rank themes by how many generated participants raised them, most important first, and some say each finding carries a confidence score from the tool and comes with recommendations, one of them prioritized. One tool’s pages mention priority rankings in its reports, and a page for freelancers lists severity ratings in the report they hand to clients, without saying how either is worked out. One offers ranking methods such as asking generated participants to pick best and worst from sets of options. We found no page describing a score that combines how serious an issue is with how many participants hit it. We found no plan named for these rankings, and one pricing page says its plans work the same apart from credits and white-⁠label reports.

Some of the tools we read say you can repeat a study over time: one reruns studies daily, weekly, monthly, or quarterly and reports standard scores such as Net Promoter Score (a customer loyalty rating), and one offers private generated audiences for tracking satisfaction, loyalty, and employee engagement across survey waves, through its sales team only. We found no engagement grade for a whole program, no predicted star rating, and no measure of how far a program moved a score, and we found no plan named for the scheduled reruns.

Some of the tools we read describe a live results view with cross-tabs (results split by group) and sharing, which one tool says comes with every study, and one offers a connector that turns a study’s live numbers into a data source for dashboards you build in an outside reporting tool, with availability depending on the plan. Some also let you ask an assistant questions about a study, such as follow-ups to the generated participants or questions across a whole project; one limits these follow-ups per study on its smaller plan, and another lists its assistant from its second plan up. We found no boards you build yourself from charts, no tile that answers a question from the board’s data, and no built-in scheduled summary emails, though one tool’s automation templates can send alert emails.

Some of the tools we read say AI chat assistants can plan, run, and read studies through a connection they offer, and some offer an API, a way for other software to read from and write to the tool, or ready-made automation templates. On plans, one lists these on every paid plan, one lists its developer interface only on its custom plan, and one lists custom connections on its largest plan only. One tool publishes templates, run in your own automation tool with its developer interface, that read a team chat channel and post results to an alerts channel, or send alert emails. Another tool’s ticket-tracker guides describe importing issues in, read only, while its feature list says an agent can return findings to an issue, so its pages disagree. One says its connection can sit beside an assistant’s other connections so a winning result can be posted to a channel. We found no ready-made link that creates tickets in the tools we read.

Some of the tools we read say an assistant or agent can take a plain-language question and set up a study, and a few say it then runs the study and writes the report too. One says its agent plans studies, runs them and generates reports, and is part of every project, though its pricing page, which lists annual plans only, shows its research agents from the second plan up. One says its agent has an autonomous mode that picks its own audiences and survey questions, and we found no plan named for it, only a booked walkthrough. One says its assistant builds the design, audience, and questions, then waits for you to launch, and comes with each of its paid plans. The others we read are tools you run yourself, sometimes with an assistant for part of the work, or services where the vendor’s team runs the study. Because the participants are generated by a model, there are no real people for these agents to invite or recruit.

Some of the tools we read say their assistant stops for your approval before it spends credits or starts a run, and one says each part of a study plan, such as the audience and the questions, waits for your approval in turn. One says its research agent has a chat mode and an autonomous mode that you switch between, and its pages name no plan for either. One shows the permissions a connected AI tool asks for, such as seeing studies or creating and running them, before you allow it. One lists its research assistant only from its second plan up. We found no page describing a setting that makes an assistant suggest, wait, or act one capability at a time.

We found no daily or weekly briefing written by an assistant in the tools we read. The closest are repeats on a schedule, which are different from a briefing. One says you can schedule a study to rerun daily, weekly, monthly, or quarterly, and that it emails whoever started a longer run when it finishes, and we found no plan named for either. Another publishes example automations that use a timer to send results to email or a team chat tool, which you assemble yourself through its programming interface (API) on its paid plans. One lists repeat survey waves on a schedule you choose as an enterprise option you ask sales about. One also publishes its own recurring public reports on categories and companies, which are not briefings on your project.

Some of the tools we read say a study will not run until you approve it. One says its assistant never launches anything itself and shows the credits for each run first, and it comes with each of its paid plans. Another, when you start interviews from a connected AI tool, shows a confirmation card with the participants and the most credits the run can use. Others have you confirm the generated participants, or the exact saved plan, before anything runs. We found no review queue in the tools we read where different kinds of work wait for a named reviewer.

We found no page in the tools we read saying that the notes you leave on an assistant’s work, such as a rejection, are kept as memory that shapes what it proposes next. The closest are different things. Some of the tools we read say you can give generated participants lasting memory from your own documents, past research, or memories you write, and one says its participants keep context from one session to the next; that same tool lists bringing in past studies and documents as a custom add-on on its pricing page, quoted for its larger plan, while its memory page names no plan. Some also say you can tell the assistant what to change and get a revised proposal within the same study.

Some of the tools we read say outside AI tools can connect through MCP, a common way to plug AI tools into other software, to plan, run, and read studies from inside the chat. Plan gating differs: one says it comes with each of its paid plans, and one says what you can do depends on your workspace and plan. We found no page describing a log of connector calls, though one says disconnecting a connected app stops its access at once and another shows the permissions a tool asks for before you allow it. Some of the tools we read keep their documentation behind a sign-in, which we could not read.

We found no way in the tools we read for you to ship your own units to participants or track them, and a generated participant is made by a model, so there is no home to send a unit to. Some of the tools we read say physical and hands-on product testing still needs real people.

We found no way in the tools we read to target specific devices, models, or software versions, and nobody holds a device in a generated study. The closest we found: some of the tools we read let you describe an audience by traits such as behaviors and brand preferences, one shows the phone system in an example breakdown of a generated audience, and one says its enterprise models can be grounded in a file of product ownership, an option you arrange with its sales team. One tutorial says the audience builder comes with every study and lists no traits by plan.

We found no field trials in real homes in the tools we read. Some of the tools we read list ethnographic studies, which look at how people behave in everyday settings, or simulate weeks of using a design, all with generated participants, and one says which study types you get depends on your plan. Others say in-home use tests and in-person ethnography belong with research done with real people.

We found no page in the tools we read about adding your own agreement, keeping signed versions, or holding a project until people sign, and we found no plan named for any of this. One says it keeps non-disclosure agreements in place with clients on its managed service, and one says generated personas need no non-⁠disclosure agreements.

Some of the tools we read say they can repeat the same questions in later waves quickly, keep a memory of earlier sessions for each generated participant, or simulate weeks of using a design. One says its respondents do not live through the weeks between surveys, so it models time explicitly. Plan details vary. One lists tracking panels among its enterprise options, one offers only a contact form for a scoped first project, and one lists bringing in your own documents as a custom add-on on its larger plan. One says in one place that audiences can be reused for tracking over time and in another that it is not a live or continuous tracking platform.

We found no way in the tools we read to ship units that are not on sale yet to participants. Most of the tools we read say you can test ideas, designs, or announcements before they are public by showing them to generated participants, and some say this means the work is not shown to real people. One says real people on real devices are still needed to find crashes before launch, and one says controls for highly sensitive concepts are arranged by contacting its team.

Some of the tools we read say their own team does the work for a customer. One describes a full service where it runs the research and brings you the results, another says it scopes a first project around one decision and runs it, and another describes its engineers working with clients to build the simulated audiences and run the surveys. Others limit their team to building an audience or a custom model from your data, preparing your files, or onboarding, and some say they are software you run yourself. Where we could see how team-run work is sold, it comes by proposal or written scope after a sales conversation, or sits on a higher plan or a custom add-on, and the pages we read give no separate price for the team-⁠run work itself.

We found no page describing a service where a team ships a customer’s product to participants, tracks it, or arranges returns. Some of the tools we read say on their own pages that questions about handling or using a physical product are for real people to answer.

Most of the tools we read say in their terms that you keep what you put in, and several say you own the results too. Several also keep the platform as theirs and let you use it under your agreement, and some keep the audiences or models built on it as theirs too, so what is clearly yours is your data and your results. Some say an audience built from your data is private to you, and one says you cannot recover your data once the terms end. One describes its team building audiences and then giving the client’s own team access to run analyses themselves, under a written scope, and its terms say continuing after a one-off project needs a new scope. Some offer onboarding or hands-on training for your team, on a higher plan or as a custom quote. We found no other page about handing a project from a vendor’s team to a customer’s own team.

Some of the tools we read mention single sign-on, which lets people log in with their company account. One lists it on its higher plans and says in its setup guide that it usually needs an enterprise plan, with its own team doing the setup by email; another lists it only on its enterprise plan. One answers yes in a list of security questions, and one shows “Contact Us” in the single sign-on row of a comparison table, which does not say whether it is supported; neither names a plan. We found no page in the tools we read saying that any of them supports SCIM, the standard that adds and removes users automatically.

Some of the tools we read describe roles for the people on a team. One lets a team choose a member or admin role when inviting people to a shared workspace, which its pricing page lists on its team plan, and it offers read-only viewers. Another lists team access with roles only on its enterprise plan. Some describe audit logs, which record who did what, in their security documents, for example access through connected AI tools and software, changes to connections and administrative actions, or study creation, exports and account changes, and none says which plan includes them. We found no help page that shows customers a log of their own team’s activity; one help page mentions an import trail for a connected ad account without saying customers can open it. Some also say their results come with a research trail that links each finding back to the answers behind it, which is a different thing from a record of who did what.

Studies in these tools run on participants generated by a model, so there are no sign-ups with names and emails to show or hide, and we found no setting that lets some teammates see personal details while others see them masked. One tool’s public changelog mentions an anonymizing filter on data you upload, another says customers are responsible for removing personal details from any data they upload, and another says it takes in no customer data or personal details, only a description of your audience and any supporting research you choose to share.

Some of the tools we read publish an API, which lets other software read from and write to them, with documentation and keys you create yourself. One adds official code libraries, and some document webhooks, which are automatic messages to other software when a study or job finishes. Some also offer a connector that lets AI assistants build studies and read results. One lists connections to design tools, spreadsheets, and automation services but marks only some of them as available. Plan gating varies. One lists its API, assistant connector, and standard integrations on its pay-as-you-go and team plans, and its documentation calls API access a paid-plan capability that stays within plan allowances. One lists API access on each paid plan, and one lists its assistant connector on each of its paid plans. One lists priority API access from its higher plans without saying which plans get the API itself, and one does not say which customers get its API. One lists API access only on its enterprise plan, while its assistant connector page gives free accounts a key. Others keep their documentation behind a sign-in, so we could not read it.