Two engines on one platform: real respondents recruited on social media, and simulated ones — with a score that says which to trust.
Johns Hopkins University
AwardAwardAwardResearch partnerStrategic partnerStrategic partnerBackerBackerMemberMemberReal respondents, recruited where they already are
81% passed the attention checks, against 37 % on a commercial panel — University of Zurich validation study II
Recruited ad hoc on Instagram, Facebook, TikTok, Snapchat, LinkedIn and WhatsApp — micro-targeted for the specific question, reaching groups no panel holds.
Simulated respondents, answering the same questionnaire
6.9/10 average plausibility in August 2026 — scored by an independent judge model
In plain words: a language model that has learned from decades of representative, proprietary survey data from established partner institutes plays the respondents. Technically, persona-conditioned large language models (LLMs), fine-tuned on that data — decades of it from gfs.bern — one model call per synthetic respondent.
Human Sampling Silicon Sampling
Thirteen polls over seven months (N 416–976), checked against two referendum results and an established Swiss panel: in 2 out of 3 political polls, Boomerang predicted the vote more closely.
Same questionnaire, USA, 1'000 respondents each: 42% vs. 20% completed both waves.
Our social-media field layer in gfs.bern's vote surveys: their forecast, our field.
Against a client's own fieldwork: results aligned with its internal benchmarks for social-media and hybrid samples (social media plus panel, all human).
0.25% off the result, against an industry average of roughly 3.6%. One race, internal analysis: a case, not an error rate.
Figures in the next sections.
Innosuisse-funded; first publication expected Q1 2027.
PDFs: Methodik · Swiss Insights News #11 · Pre-Study 2021 · Study 2023/24 · More: Forschung & Validierung
A survey by gfs.bern for SRG. gfs.bern, a strategic investor in Boomerang since 2023, uses Boomerang as one part of the sample for the SRF trend surveys (quarterly).
gfs.bern's own note on the method. gfs.bern uses Boomerang as one part of the sample for the SRF trend surveys (quarterly).
On how gfs.bern surveys. gfs.bern uses Boomerang as one part of the sample for the SRF trend surveys (quarterly).
The article's method box names Boomerang Ideas as the company that ran the survey.
Interview with our founder, Raphael Ueberwasser.
An episode about Boomerang Ideas with our founder.
On our first investment round. Boomerang has been seed-backed since 2020.
On our seed round and Innosuisse research funding. Innosuisse has funded Boomerang several times since 2020.
Lists our seed round. Boomerang has been seed-backed since 2020.
The distance between the answers real people gave and those our production model produces.
82.6%
Boomerang Model — mean overlap with the human answer distributions
<52%
Score of untuned frontier models on the same test
Every result carries a plausibility score, and the platform says when to ask real people instead.
0low10high
Human samples feed back into the model, so the curve climbs.
Fig. 2 — Average plausibility per month, Dec 25 – Aug 26; everyday use, scored against every human sample we field.
Easy, fast and affordable high-quality research — for campaigns, product features, strategy and vote forecasts. The method changes with the question; the standard doesn't.
Joint research with Universität Basel and Johns Hopkins University, funded by Innosuisse; two earlier validation studies with Universität Zürich.
Sampling methods, quality standards, benchmarks and Confidence Guard™ — maintained once, used by both outlets.
One scientific standard and one data-ethics line for both outlets.
Boomerang Ideas AG, Zürich — founded 2021. Institute member of Swiss Insights, in strategic partnership with gfs.bern and Panter. A small team that runs its own field and its own benchmark, on models we run ourselves — and publishes the misses along with the hits.
Partners & memberships
Hadrien Jean-RichardDigital Marketing
Christophe NuñezSalesDr. Andrea Bublitz (Academic Advisor · Universität Basel)
Dr. Linda Stougaard Nielsen (AI / ML Advisory)
Dr. Kristina Gligorić (Ethical AI Advisor · ex-Stanford, now Johns Hopkins)
Katia Murmann (Product Advisory)
Beat Seeliger (Technology Advisory · Panter)
Martin Steiger (Data Privacy Advisory · Steiger Legal)
Peter Erni (Social Media Advisory · Brain & Heart CEO)
Trevor W. Goodchild (Facebook Strategist)
Martin Brettenthaler (Sales Strategy)
Daniel Vogler (Marketing Advisory)
Jean-Marc Hensch (Angel investor · strategic advisory · Vice Chairman)
Research collaborations, methods questions, media: hello@boomerangideas.com. Projects and quotes run through the two doors at the top of the page.
⠿ verschieben
Als anonym