Give your LLM
a synthetic persona
it hasn't seen before.

Generate statistically-grounded synthetic personas with sub-20ms server-side generation. 62 dimensions. Hierarchical demographic dependencies. The human variety that language models quietly erase — restored.

See the API →
Kristy Carmichael
persona_7k2m9f4q1x8d3b6n
28 yrs
Female
California
Software Eng.
Liberal
Age
28
Education
Bachelor degree
Openness
High openness
Income
75k to 100k
Tech comfort
Comfortable user
Height
5'6"
Interests
Trail runningPodcasts
Languages
EnglishSpanish
+ 57 more dimensions in JSON
Generated
Sarah Chen28 · Software Engineer · California
Marcus Williams45 · Construction Manager · Georgia
Ingrid Johanssen67 · Retired Teacher · Minnesota
Priya Nair31 · Marketing Lead · New York
Bob Henderson71 · Retired · Minnesota
Ana Reyes22 · Student · California
James Kowalski55 · Factory Supervisor · Ohio
Chen Wei34 · Software Engineer · New York
Dorothy Walsh58 · Nurse Practitioner · Pennsylvania
Luis Mendoza38 · Truck Driver · Texas
Sarah Chen28 · Software Engineer · California
Marcus Williams45 · Construction Manager · Georgia
Ingrid Johanssen67 · Retired Teacher · Minnesota
Priya Nair31 · Marketing Lead · New York
Bob Henderson71 · Retired · Minnesota
Ana Reyes22 · Student · California
James Kowalski55 · Factory Supervisor · Ohio
Chen Wei34 · Software Engineer · New York
Dorothy Walsh58 · Nurse Practitioner · Pennsylvania
Luis Mendoza38 · Truck Driver · Texas
62
Demographic dimensions per persona
158k
Statistical data points
<20ms
Typical server-side generation
99.9%
Uptime SLA
Why it exists

Random users
aren't random enough.

Ask a language model to imagine a typical customer and you'll get the same person every single time: educated, urban, moderate, tech-savvy. Real populations are messier. That messiness is the point.

LLMs have demographic blind spots

Without grounding, AI defaults to an average. A 65-year-old rancher from rural Montana responds to healthcare messaging very differently than a 28-year-old software engineer in San Francisco. Generic personas flatten that difference to nothing.

Demographics interconnect systematically

Surgeons rarely earn minimum wage. Geographic location shapes career access. Educational opportunity correlates with socioeconomic background. PersonaGen models these dependencies — not just random attribute combinations.

Scale that focus groups can't

Test your product message across 200 demographic combinations in the time it takes to schedule a single focus group. No researcher bias. No recruitment costs. Just statistically-grounded variation at API speed.

How it's built

Built from the
ground up.

Every persona is constructed through a five-stage dependency pipeline — earlier traits constrain later ones, then physical attributes are enriched into concrete measurements.

1
Foundation
Fixed at birth

Immutable traits that constrain everything downstream. Set first, never overridden.

Country of birthEthnicityBiological sexGenetics
2
Life path
Shaped by Tier 1

Constrained by context of birth — access, family structure, regional opportunity.

EducationChildhood SESFamily structureGeography
3
Career & income
Influenced by 1 + 2

Occupation and income constrained by education, location, and socioeconomic origin.

OccupationIncome bracketEmployment typeIndustry
4
Psychology & lifestyle
Shaped by all above

The full persona — personality, interests, habits — with AI-augmented nuance layered on a statistical base.

Big FivePolitical leanInterestsTech comfortReligion
5
Physical profile
Derived + enriched last

Physical attributes are generated after health and activity context, then enriched into concrete height, weight, and descriptive profile details.

Body buildHair / eyes / skinHeight + weightPhysical description
Try it

Generate a persona.
One in 10+ septentrigintillion.

api.personagen.dev/v1/persona
Ready
Request
Unauthenticated limit (IP)
0 / 25
No API key: 25 requests per hour.
Sign in to create an API key for higher limits.
Press generate
62 dimensions · statistically grounded
sub-20ms server-side generation
Use cases

What you can
build with it.

Journey mapping
Mapping customer journeys across demographics

How do different people research and decide on a $500 purchase? Feed the same question through varied personas and the decision patterns become your research.

@Kristy, 28, CA@Darnell, 45, GA@Evelyn, 67, MN
Simulated responses
Kristy Carmichael · 28 · Software Engineer
Goes straight to Reddit and YouTube reviews. Filters by "best value under $500" then checks return policy before committing. Decision in 45 minutes.
Darnell Price · 45 · Construction Manager
Asks a coworker who bought one last year. Visits Best Buy to hold it, then calls his carrier on compatibility. Decision takes a week.
Evelyn Walsh · 67 · Retired Teacher
Goes to the carrier store for in-person help. Main concern is photo transfer and readability. Decision is same day if onboarding feels simple.
For developers

One request.
Full persona.

A single GET request returns a complete, statistically-coherent human being. No API key is needed for limited traffic, and API keys unlock higher monthly limits.

What's included
Total dimensions62
Unique values982
Seed supportYes — reproducible
CoverageUS (UK coming soon)
No key limit25 / hour (per IP)
API key default5,000 / month (higher on request)
Request · bash
# One line. Full persona.
curl -X GET "https://api.personagen.dev/v1/persona" \
  -H "X-API-Key: pk_your_key_here"
Response · json (truncated)
{
  "success": true,
  "data": {
    "name": {
      "first": "Sarah",
      "last": "Chen"
    },
    "demographics": {
      "age": 28,
      "location": "California",
      "// + 15 more dimensions"
    },
    "psychology": { "// 7 dimensions" },
    "lifestyle":  { "// 21 dimensions" },
    "physical":   { "// 15 dimensions" }
  },
  "metadata": {
    "id": "persona_7k2m9f4q1x8d3b6n",
    "generated_ms": 84
  }
}
FAQ

Questions
worth asking.