Synthetic respondent platforms generate survey or interview answers from AI respondents instead of recruited people. The ten vendors below differ most in two things: where the respondents come from (public sources, behaviour data, your own surveys, or a short audience description) and how you can check the answers against real people. Minds, our product, builds reusable Audiences you run Studies on yourself and scores each Audience against real surveys; Aaru, Simile, Electric Twin and Artificial Societies are enterprise simulators with their own published benchmarks; Evidenza is a managed B2B service; Qualtrics Edge Audiences sits inside Qualtrics; Synthetic Users, SYMAR and Lakmoos cover narrower jobs.
Prices and accuracy figures were checked on vendor pages in early October 2026 and link to their source. There is no independent rating body for this category yet, so "top rated" here means the vendors with the most checkable evidence, not a review score. If you first need the concept itself, read what synthetic respondents are. For the broader list of research tools, including GWI, Fairgen and Yabble, see the best synthetic market research tools.
| If you need… | Pick | Why |
|---|
| Self-serve Studies on reusable Audiences, qualitative and quantitative, checked against real surveys | Minds | Pay as you go and published prices; each Audience gets a validation score per survey |
| Population answer shares for a large strategic decision, delivered by the vendor | Aaru | Largest published validation set: 2,993 questions from 186 studies |
| Individual-level behaviour simulation with a confidence estimate per result | Simile | Builds on the Stanford 1,000-people research; enterprise only |
| An always-on synthetic copy of your own survey audience | Electric Twin | Models your survey data and tests it on a holdout before go-live |
| How opinion spreads through a stakeholder network | Artificial Societies | Connected persona networks of 12 to 3,500 personas |
| Hard-to-reach B2B executives, as a managed service | Evidenza | Results within 72 hours, no customer data required |
| Synthetic responses next to human panel data in Qualtrics | Qualtrics Edge Audiences | Synthetic answers flagged in the same dataset |
| Synthetic interviews for product discovery | Synthetic Users | Interview transcripts plus an insights report |
| Low-cost synthetic surveys and focus groups for a single project | SYMAR | Self-serve from EUR 99 a month |
| A private AI panel tuned on your own data | Lakmoos | Custom panel model; per-survey spend published |
The source of a synthetic respondent decides what it can answer well. Four approaches dominate:
- Population models from behaviour and outcome data. Aaru and Simile train on large sets of real-world behaviour, transactions or study responses and simulate a population. Strong for market-level questions; you rarely see an individual persona's reasoning.
- Twins of your own survey audience. Electric Twin and Qualtrics Edge Audiences learn from survey data (yours, or Qualtrics' own) and answer new questions in the same population. Strong when you have a deep survey history; weaker for audiences you have never surveyed.
- Reusable Audiences grounded in public sources and your inputs. Minds builds each Audience from public-source context, the files and briefs you add, and an editable demographic mix, then keeps those Minds for every later Study. Strong for frequent concept, copy and prototype tests across many audiences.
- Prompted personas from an audience description. Evidenza, Synthetic Users, SYMAR and many smaller tools start from a described audience. Fast to set up, but the quality depends on how well the vendor validates the result.
| Platform | Respondent source | Interaction | Starting price | Published accuracy / validation |
|---|
| Minds | Public sources plus your files and briefs | 1:1 interviews, group discussions, questionnaires, 11 methods | Pay as you go ($0.12/response); Pro $199/user/month | Per-Audience validation score out of 100 against real surveys |
| Aaru | Behaviour and outcome data | Population simulation, crosstabs | Not published (source) | Pearson 0.970 across answer shares, 186 studies (source) |
| Simile | Proprietary studies and behaviour data | Behaviour simulations with predicted confidence | Not published (source) | 83% to 86% of human retest consistency in the underlying research (source) |
| Electric Twin | Your own survey data | Questions, creative tests, focus groups | Not published (source) | 92% on its distribution measure vs a 94% human retest ceiling (source) |
| Artificial Societies | Public data of real individuals, linked in networks | Society simulations, surveys | Not published (source) | 86% distribution accuracy across 1,000 surveys (source) |
| Evidenza | Audience description | Managed surveys and interviews | Not published (source) | 88% similarity score across 100+ tests, metric not defined (source) |
| Qualtrics Edge Audiences | Qualtrics survey data | Synthetic panels of 50 to 10,000 responses | Credits via sales (source) | 0.07 SD average deviation on 8 Likert items (source) |
| Synthetic Users | Audience attributes plus your data | Synthetic interviews | From $12,500/year (source) | 85% to 92% theme parity in vendor studies (source) |
| SYMAR (formerly OpinioAI) | Persona and segment definitions | Synthetic surveys, focus groups, interviews | From EUR 99/month (source) | Not published |
| Lakmoos | Proprietary model plus your private data | AI panel surveys and interviews | $200 to $500 per survey, typical client spend (source) | Vendor reports over 98% in 20 benchmarking studies, metric not defined (source) |
Vendor accuracy figures measure different things (correlation, distribution overlap, theme recall, standard deviation), so do not rank platforms by comparing them. Read synthetic audience validation and accuracy compared before you weigh any percentage.
Minds
Best for: insights, marketing and product teams that want to run synthetic Studies themselves and check each Audience against real data.
Minds is the end-to-end platform for commercial synthetic research. You create an Audience, a reusable set of Minds (AI personas) grounded through Minds PRISM in public sources and the research inputs you add, then run Studies on it: one-to-one interviews, moderated discussions, or questionnaires with open-ended, single choice, multiselect and scale questions. Eleven deterministic methods run inside a Study, including MaxDiff, NPS, key driver analysis, TURF, Van Westendorp, Kano and conjoint. You can test copy, websites, images, video, decks and Figma inputs where enabled, compare segments, and export results or reach them through the API and MCP.
Pricing: Pay as you go at $0.12 per response; Pro $199 per seat a month with 5,000 pooled responses per seat per month; Enterprise custom.
Validation: Audience Validation scores an Audience against real published surveys or your own survey files, with a score out of 100 and a 95% range per survey, and lists the questions it left out. See the methodology.
Limits: answers are directional, not a representative sample; there is no recruited panel; validation needs at least 10 ready Minds and enough fitting survey questions, which is hard for niche B2B roles.
Aaru
Best for: enterprises that want the vendor to simulate a population for pricing, product or communications decisions.
Aaru simulates populations of agents grounded in behaviour and outcome data. On September 30, 2026 EY announced an alliance with Aaru to bring behavioural simulation to its clients, following a blinded recreation of EY's Global Wealth Study (source). Its September 2026 validation study covers 2,993 questions and 13,727 answer shares from 186 studies (source).
Limits: sales-led, no self-serve access, and the benchmark is vendor-published. Compare in Minds vs Aaru.
Simile
Best for: enterprises simulating customer or population behaviour where a confidence estimate per result matters.
Simile trains a model of human behaviour on large proprietary studies and attaches a predicted confidence to each simulation. It raised over $200 million at a $2 billion valuation and names CVS Health, Wealthfront, Deloitte and Gallup as customers (source). The research it builds on reproduced General Social Survey answers at 83% to 86% of participants' own two-week retest consistency (source); that is an academic benchmark, not a test of the commercial product.
Limits: enterprise only, pricing not published. Compare in Minds vs Simile.
Electric Twin
Best for: enterprises with their own survey data that want an always-on synthetic version of their audience.
Electric Twin builds synthetic audiences from a customer's surveys and runs a holdout evaluation on that data before go-live. It reports 92% on its normalised distribution accuracy measure against a 94% human test-retest ceiling (source).
Limits: accuracy depends on how much seed data you bring; demo-led pricing. Compare in Minds vs Electric Twin.
Artificial Societies
Best for: communications, reputation and investor-relations teams asking how opinion forms and spreads.
Artificial Societies links personas, grounded in real individuals' public data, into social networks of 12 to 3,500 personas. Its January 2026 evaluation reports 86% distribution accuracy across 1,000 real-world surveys, against a 91% human self-replication ceiling (source).
Limits: coverage skews to people with an online footprint; ask which network edges are observed and which are inferred.
Evidenza
Best for: B2B go-to-market teams researching hard-to-reach executives, delivered as a service.
Evidenza generates synthetic copies of target buyers, surveys and interviews them, and delivers a go-to-market plan within 72 hours of research starting, without customer data (source). It reports an 88% similarity score across 100+ head-to-head tests but does not publish what the score measures (source).
Limits: managed rather than self-serve. Compare in Minds vs Evidenza.
Qualtrics Edge Audiences
Best for: existing Qualtrics customers who want synthetic responses next to human panel responses.
A model trained on Qualtrics survey data produces synthetic panels of 50 to 10,000 responses, currently for the US general population in English, flagged as synthetic in the dataset (source). Qualtrics reports an average deviation of 0.07 SD from human means on eight attitudinal Likert questions (source).
Limits: one published benchmark on eight questions; US English only for now.
Synthetic Users
Best for: product and UX teams running early synthetic interviews.
Synthetic Users runs interviews with AI participants and writes an insights report; studies can be grounded in your transcripts and support tickets. Annual plans start at $12,500 a year on a research-token pool, and vendor-run comparisons report 85% to 92% parity with real interviews on thematic overlap (source).
Limits: theme overlap is not distribution accuracy; interview-led. See Synthetic Users alternatives.
Best for: individual researchers and small teams running a single synthetic project.
SYMAR builds synthetic personas and segments and runs synthetic surveys, focus groups, interviews and creative evaluations. The Project plan starts at EUR 99 a month (source).
Limits: no published validation figure. See SYMAR alternatives.
Lakmoos
Best for: enterprise and agency teams that want a private AI panel tuned on their own data.
Lakmoos, a European research company, adds a client's private data to its model, builds a custom reasoning panel and answers surveys and interviews from it. It says clients typically spend $200 to $500 per survey and that its panels scored over 98% in 20 benchmarking studies, without defining the metric (source).
Limits: the accuracy figure cannot be compared with other vendors' numbers until the metric is published.
Earlier versions of this page listed Perspective AI. Perspective AI (getperspective.ai) turns static web forms into AI-led conversations with real people, for lead qualification, applications and onboarding (source). It collects real answers rather than simulating respondents, so it is no longer compared here.
Work through four questions before a demo:
- Who do you need to simulate? If you have years of survey data on the audience, a survey-trained twin is a natural fit. If you test many different audiences, including ones you have never surveyed, reusable Audiences grounded in public sources and your files are more practical.
- What will the answer be used for? Screening ten concepts down to two tolerates directional evidence. A board-level investment case needs the vendor's validation on your own topic plus a human or in-market check.
- Can you inspect the evidence? Ask to see the persona definition, the full transcript or answer set, and how distributions are calculated. A chart with no underlying answers cannot be audited.
- Who runs the work? Managed services (Evidenza, Aaru) suit occasional, high-value questions. Self-serve platforms (Minds, SYMAR, Synthetic Users) suit weekly testing by the team that owns the decision.
For data provenance and procurement questions, see synthetic audience data sources compared and synthetic research security and procurement.
- Final go or no-go on a large launch. Confirm with a human study or an in-market test.
- Statistically representative estimates such as market size or share that will be quoted as facts.
- Physical and sensory products where taste, smell, fit or handling matter.
- Regulated claims such as health or financial claims that need substantiation.
- Behaviour measurement such as clicks, conversion or purchase.
- Audiences with little public data, such as rare conditions or niche B2B roles, unless you can add your own research.
A practical rule: rerun a past study where you already know the human answer, compare the synthetic result, and only rely on the platform for decisions of that kind once it holds up.
We compared each platform on respondent source, interaction modes, published validation and what it measures, pricing transparency, and self-serve versus managed delivery. Every price and accuracy figure links to the vendor page or paper we read in early October 2026; "not published" means we could not find one. No vendor paid for placement. Minds is our product; we list it first where it fits and state its limits. The Minds research hub collects our own validation work.
