Vectorial AI
SAPIENS · Human Behavior Simulation
Patient & clinician populations

Every persona in the simulation traces back to real people

Vectorial has built a proprietary behavior model — SAPIENS. Each population is learnt from the behavior of thousands of real patients and clinicians — drawn from public and enterprise sources, and from self-reported care experience acquired privately through licensed research panels.

Live population sample Sampling the population ↓
Reddit condition communities Mayo Clinic Connect Licensed research panels Licensed panel interview Inspire HealthUnlocked
95,000+
Patients modeled across live healthcare deployments
Midi Health · Everkind · Suki AI
15,000+
Clinicians modeled — nurses, NPs, physicians and specialists
11 roles & specialties
75+
Behavior traits modeled across every patient population
Per population
1,335
Simulations in a single month, replacing 1,000+ annotator hours
Everkind · 20% more holistic evaluation
01 · Grounded in real data

Every patient is modeled four dimensions deep — grounded in real user data.

Behavioral depth is the constraint in this category, not model architecture. Every dimension is derived from the behavior of real people.

Background & Demographics

Culture, community, belief systems, household composition and income tier.

e.g. tier-2 income, caregiver household, faith-informed care views
Behavior Traits

Risk tolerance, trust, adherence, care-seeking style.

e.g. peer-verifies before acting, cost-sensitive, low institutional trust
Environment & Scenarios

The settings a patient moves through — outpatient, inpatient, discharge — and how behavior changes across them.

e.g. post-dismissal, pre-diagnosis, researching alternatives at night
Experience & Exposure

The journey to date and prior exposure to treatments, clinicians and competing solutions.

e.g. two GPs seen, HRT declined once, currently self-treating
01.1 · Where the data comes from

We model population behavior from a wide variety of data sources — a highly accurate representation of how people actually behave, built while preserving anonymity throughout.

SUPPLY 01

Public behavioral signal at scale

A deep research agent finds the communities where a given patient or clinician population actually congregates, then ingests what they say unprompted — the closest thing to observing behavior without interrupting it.

Patient & clinician communities Reddit condition communities Mayo Clinic Connect HealthUnlocked Inspire Patient.info forums Drugs.com reviews WebMD drug reviews YouTube patient vlogs r/medicine r/nursing allnurses Student Doctor Network Medscape discussions
SUPPLY 02

Licensed panel interviews

We run our own structured interviews with real people in the condition area, through licensed panels, at a volume no research team can staff. These are ingested as first-party signal and used to enrich thin traits and correct the ones public sources got wrong.

First-party, consented Licensed panel interviews Recorded transcripts Consented respondents
SUPPLY 03

Licensed research panels

Panel integration gives access to verified, consented respondents with known demographics — including healthcare-specialist panels of screened patients, caregivers, nurses and physicians. Used to fill gaps where a population is under-represented online, most often older, rural and lower-income patients.

Verified panels Prolific Sermo · physicians M3 Global Research · HCPs Rare Patient Voice · patients & caregivers CloudResearch Connect Dynata Health Coverage correction
01.2 · Patient journey

Modeled across the care journey — outpatient, inpatient and discharge.

The same patient behaves differently in a clinic room, on a med-surg floor and on a discharge call. Every modeled patient carries a journey stage, and the simulation runs inside it.

Care setting drives behavior
OUTPATIENT
Clinic & pre-admission
Symptom history, self-research, consent and pre-op instructions — patients under-report and arrive with forum knowledge
INPATIENT
Admitted & post-op
Assessments, pain and mobility, flowsheet intake — patients hedge, guard, and can’t name what they feel
DISCHARGE
Transition home
Medication and activity instructions, home setup, what they didn’t ask before leaving
POST-DISCHARGE
Recovery & follow-up
Follow-up calls, adherence, when to escalate — and the questions they take back to communities instead

A knee-replacement patient describes pain very differently on post-op day two than on a week-two follow-up call. Pinning the simulation to the setting is what makes the transcript usable.

02 · Patient populations

Populations running inside health companies today.

Live deployments — actual audiences, with profile counts, signals and readiness as the platform reports them.

Midi Health
Virtual care clinic for women's health — perimenopause, menopause, postpartum, obesity, hair loss
30,000+
Patients modeled
75+
Behavior traits
350+
Clinical journeys
Sample modeled audiences
Perimenopause skincare users
Readiness91/100
Profiles 1,164Signals 68,407Trait groups 7
Perimenopause women
Readiness94/100
Profiles 1,200Signals 55,575Trait groups 7
Postpartum women
Readiness92/100
Profiles 1,128Signals 98,887Trait groups 7
General women's health
Readiness89/100
Profiles 1,064Signals 47,633Trait groups 7
Everkind
AI therapist platform — mental health patients, therapists and psychiatry
50,000+
Patients modeled
15+
Patient populations
1,000+
Annotator hrs replaced
Sample modeled audiences
Mental health patients
Readiness93/100
Profiles 5,498Populations 15+Trait groups 7
First responders
Readiness90/100
Profiles 1,466Signals 44,292Trait groups 7
Women in mid-life transition
Readiness91/100
Profiles 264Signals 35,978Trait groups 7
Relationship tension & breakdown
Readiness88/100
Profiles 420Signals 23,500Trait groups 7
Suki AI
Ambient clinical intelligence — modeling both sides of the encounter: surgical patients and the nurses who care for them
15,000+
Patients modeled
5,500+
Clinicians modeled
60+
Recovery journeys
Sample modeled audiences
Knee surgery patients
Readiness92/100
Profiles 836Signals 36,912Trait groups 7
C-section recovery
Readiness90/100
Profiles 752Signals 29,480Trait groups 7
Nurse practitioners (NP)
Readiness93/100
Profiles 1,008Signals 31,860Trait groups 7
Registered nurses (RN)
Readiness91/100
Profiles 1,256Signals 38,417Trait groups 7
03 · Clinician populations

Every clinician brings a protocol and a personality. We model both.

Every clinician has their own philosophy, bedside manner and workflow constraints. Populations are built from the communities where clinicians actually talk to each other, and every behavioral dimension is confidence-scored.

Live clinician sample · 94 modeled clinicians across 11 roles Sampling the population ↓
r/medicine & specialty subs Medscape discussions Student Doctor Network Licensed panel interview Nurse & NP communities Prolific · verified clinicians
Modeled clinician audiences
Built from the communities where each role actually talks
9,500+
Clinicians modeled
11
Roles & specialties
Sample modeled audiences
Psychiatrists
Readiness92/100
Profiles 890Signals 28,235Trait groups 7
Therapists
Readiness90/100
Profiles 900Signals 16,429Trait groups 7
Nurses
Readiness91/100
Profiles 1,224Signals 33,148Trait groups 7
Dentists
Readiness89/100
Profiles 772Signals 19,764Trait groups 7
04 · Why it’s different

A grounded population, not a language model playing a patient.

Ask an LLM to play a patient and it gives you the reasonable answer — that is what it was trained to do. SAPIENS audiences are learnt from the observed behavior of real patients and clinicians, so they carry the delay, distrust, cost-driven choices and messy self-reporting an LLM persona smooths away.

 
Prompted LLM persona
SAPIENS grounded profile
Origin
Written from a prompt or a segment definition
Learnt from the observed behavior of real patients in that condition area
Diversity
Reproduces the assumptions in the prompt and regresses to the median patient
Distribution inherited from the real population — culture, income tier, care access, prior treatment history
Backstory
Invented to sound plausible
Grounded in real journeys: when symptoms started, who was seen, what was tried, what failed
Context
Answers in a vacuum, the same way every time
Answers from inside the journey stage that patient is actually in
Under pressure
Agrees, normalizes, and gives the rational answer
Holds the avoidance, distrust and cost-driven behavior that real patients show
Auditability
No provenance — you cannot ask where an answer came from
Every trait traces back to signals and interview transcripts, each confidence-scored
Measured accuracy · SAPIENS vs LLMs

SAPIENS vs general purpose LLMs on human behavior simulation benchmarking

Accuracy is the % of opinions where the real opinion matches the generated one when a product is shown to a modeled user.

frontier ceiling
53.2%
47.6%
49.95%
51.2%
50.4%
49.85%
86.1%
o3Apr 2025
GPT-5Aug 2025
GPT-5.2Dec 2025
Sonnet 4.6Feb 2026
GPT-5.5Apr 2026
Opus 4.8May 2026
SAPIENS 

Benchmark developed with Berkeley AI Research (BAIR). Full methodology, per-model and per-domain results: SAPIENS Benchmarking Study.

05 · Testimonials

Judged by experts

”
When Vectorial showed the perimenopause audience simulations, it immediately made sense to us. The model captured behavioral traits and motivations consistent with what we see from real patients, which made the audience feel credible and specific.
Laura Moon
VP Product · Midi Health
”
Vectorial's value is that the personas are learnt from real people, not synthetic data — something no evaluation engine has been able to close the gap on for us in a meaningful way.
Supreet Pal Singh
CTO · Everkind
”
The chat simulations feel natural, representing real-life conversations between patients and nurses — and they are clinically accurate.
Nandita Kamath
Director of Nursing Solutions · Suki AI