Take Product UX decisions before you build and launch.
A living simulation of people grounded in real behavioral data — modeling how each person behaves and the environment they decide in, to predict how they’ll respond to your product.
Individual & collective behavior
How Vectorial solves it
We model the individual, their variance, and the collective.
Each person is modeled from traits that travel together — age with risk appetite, income with geography — and the collective keeps a real market’s variance: no two people identical, no segment reduced to its average.Grounded in observed behavior, not invented personas.
Stated vs. actual behavior
How Vectorial solves it
We model their actions, not just their answers — thousands of live sessions across product, agents, and campaigns.
Unstated needs surface only in behavior. Your population uses the real interface, talks to your agent, and reacts to the campaign — so you watch where they stall, what they misread, and which message loses which segment.Thousands of sessions. Zero real customers at risk.
Environment & scenario
How Vectorial solves it
We model the situations and triggers around every interaction.
People come to you at a moment — a promotion, a move, a new baby, a crisis at home. We model the situations around your population and the triggers that push someone to act, so every interaction starts from a real reason.Real moments and real reasons — not random traffic.
Intrinsic & extrinsic drivers
How Vectorial solves it
We encode both — the person and the world around them — from vast behavioral data.
Every individual is encoded from layers of data — backstory, circumstances, structured facts, social and public signals, and the environment around them — drawn from panels, proprietary partners, and the public domain.20+ data partners · millions of real behavioral signals.
Share of opinions where the generated opinion matches the real one. Frontier models sit near chance; dedicated behavior models do better, and still trail.
Opinion-match rate is a separate measure from the 87% behavioral-accuracy figure reported in our benchmarking study — that one is scored across held-out real-world studies rather than on the product-shown task. Comparison models evaluated on identical prompts and the same held-out opinion set.
Straight answers on how SAPIENS differs from a prompted LLM, where we sit against the alternatives, and where we'd tell you to use real people instead.
The differentiation is the substrate. Vectorial models users from vast real behavioral data. Traditional research asks real people — but only a handful of them. AI-moderated interviews scale the moderator, not the sample. Other synthetic users invent people with no data underneath. Here's where each one actually wins.
| Traditional research | AI-moderated interviews | Other synthetic users | Vectorial | |
|---|---|---|---|---|
| Time to read-out | 4–8 weeks | 5–10 days | Minutes | Hours |
| Typical sample | 8–30 recruits | 50–200 recruits | Unbounded, but ungrounded | Hundreds to thousands, sampled to a real distribution |
| Who is answering | Real people — the gold standard | Real people, AI moderator | An LLM improvising a persona per call | A modeled person grounded in real behavioral data — at the scale of thousands |
| Hard-to-reach segments | Expensive, sometimes impossible | Same recruiting constraints | Free, but invented | Modeled from behavioral data — clinicians, patients, niche cohorts |
| Traceability | Transcripts and recordings | Transcripts | None — no substrate to inspect | Every trait traced to the signals that produced it |
| Typical cost | $10,000+ for one study | $5,000+ for one study, with 2× more users | Low, but ungrounded | ~$100 per simulation, with 100× more users |
| Best used for | Final validation, ethnography, regulated claims | Qualitative depth at moderate scale | Quick gut-checks you wouldn't bet on | User research across the entire product development lifecycle |
Ranges reflect commonly reported industry timelines and typical engagement sizes; your mileage varies by category and recruiting difficulty.
No. The difference is what sits underneath: Vectorial models users from vast real behavioral data — panels, transactions, observed choices — then samples a population and reports the distribution. A prompted LLM improvises one plausible individual from no data at all. Same prompt, two very different objects:
Put simply: an LLM tells you what a person might say. SAPIENS tells you how many would say it, which ones, and why.
SAPIENS is validated the way any model should be: against held-out real-world outcomes it never saw during training. Across those benchmarks it reproduces observed behavioral patterns with 87% accuracy, and we publish the methodology rather than asking you to take the number on faith.
Accuracy is not uniform, and we'd rather you know where it isn't. The model is strongest on preference ordering, relative message resonance, and directional price sensitivity — the questions most teams are actually trying to answer. It is weaker on absolute point predictions in categories with thin behavioral data. When confidence is low for a given question, the read-out says so.
Yes — and this is where the compounding starts. Bring your CRM segments, past survey waves, panel data, support transcripts, or product analytics, and we fit a population that matches your market's actual composition rather than a generic national one.
Calibration also gives you a back-test: we hold out a study you've already run in the real world and check whether the population reproduces it before you trust it with a new question.
When the finding has to hold up to an outside party — regulatory claims, clinical evidence, litigation, anything where provenance of a human subject is the point. When you're exploring a space so new that nobody's behavior in it has been observed yet. And when the value is in the room itself: watching someone struggle with a device tells you things no model will surface.
The teams who get the most out of Vectorial use it to arrive at their real-world research with sharper hypotheses and fewer wasted sessions — not to cancel it.
Your data trains your population and nothing else. It is never used to train shared or foundation models, never pooled across customers, and never sold. Everything is encrypted in transit and at rest, access is scoped by role with full audit logging, and SSO/SAML is available on enterprise plans.
Vectorial is SOC 2 Type I and ISO 27001 certified. For healthcare work, populations are modeled from de-identified behavioral patterns — you don't need to send us PHI to get a calibrated population, and in most engagements you shouldn't.
SOC 2 Type I and ISO 27001 — plus the controls security teams actually ask about.
SOC 2 Type ICertified 
ISO 27001Certified Spin up a population that matches your market and start asking questions today.
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