Every row in every cohort on this site is the same deep object: identity, a dated career history, education with degree geography, evidence assets, verified contacts, and per-route scoring. Not a name-and-email pair — a case file that arrives before the consult. This page shows the object exactly as it ships: layer by layer, then a real (masked) example, then every field.
the object below is a real pull · identity masked for publication · MEASURED · Outricher data
Read directly from the professional-profile graph — the structural layer every cohort is selected on.
A multi-source waterfall adds contacts and evidence. Verified-or-empty — a blank field is a promise, not a failure.
The analytical layer our pipeline computes: archetypes, routes, criteria counts, ranks — the triage a practice starts from.
One row from the shipped founders cohort — a cybersecurity founder on the classic F2 arc (foreign undergraduate degree → US graduate school → US company). Surname reduced to an initial, employers and contacts masked for publication; the delivery ships this object unmasked.
{
"profile_id": "10########",
"full_name": "S. G. ⟨masked for publication⟩",
"headline": "Founder and CEO, ⟨company⟩ | Cybersecurity Aficionado | Speaker",
"location_name": "Washington DC metro area",
"location_country_code": "US",
"current_title": "Founder and Chief Executive Officer",
"current_company": "⟨cybersecurity firm — 215 employees, DC metro⟩",
"connections": 500,
// the archetype the pipeline routed this person into
"kept_as": "F2_foreign_ed",
"visa_route": "EB1A_NIW_selfpetition",
"criteria_met_count": 5,
"lead_score": 210,
"final_rank": 210,
"has_foreign_education": true,
"foreign_universities": [
"Indian Institute of Technology, Kharagpur"
],
"background_has_prestige": true,
"prestige_source": "known_company",
"total_experience_years": 34,
"us_experience_years": 31,
"positions_count": 17,
"experiences": [
{
"title": "Founder and Chief Executive Officer",
"company": "⟨cybersecurity firm — 215 employees, DC metro⟩",
"location": "Reston, VA",
"start_year": 2001,
"end_year": null,
"is_current": true
},
{
"title": "VP Sponsorship",
"company": "⟨earlier employer⟩",
"location": null,
"start_year": 2010,
"end_year": 2011,
"is_current": false
},
{
"title": "Executive Vice President",
"company": "⟨earlier employer⟩",
"location": null,
"start_year": 2000,
"end_year": 2001,
"is_current": false
},
{
"title": null,
"company": "⟨earlier employer⟩",
"location": null,
"start_year": 2000,
"end_year": 2001,
"is_current": false
},
{
"title": "Vice President",
"company": "⟨earlier employer⟩",
"location": null,
"start_year": 1998,
"end_year": 2000,
"is_current": false
}
],
// the F2 arc on record: foreign undergrad → US graduate school
"education": [
{
"school": "University of Maryland",
"degree": "MS",
"field_of_study": "Electrical Engg - Specialization: Computer Engineering",
"start_year": null,
"end_year": null
},
{
"school": "Indian Institute of Technology, Kharagpur",
"degree": "B.Tech.",
"field_of_study": "Electronics & Communication Engg",
"start_year": null,
"end_year": null
},
{
"school": "University of Maryland",
"degree": "PhD",
"field_of_study": "Electrical Engg - Focus on Computer and Data Security",
"start_year": null,
"end_year": null
}
],
// verified-or-empty — masked here, full in the delivery
"db_personal_emails": [
"s***@***.com",
"s***@***.com"
],
"db_personal_numbers": [],
"db_profile_updated_at": "2026-06-10",
"db_profile_activity_at": "2026-02-27",
"api__source": "enrichment",
"api_followersCount": 4306,
"api_about": "Responsible for establishing corporate strategy and vision, shaping corporate culture and cultivating business relationships to enable ⟨company⟩ to thrive. Extensive back…",
"api_location": {
"city": "⟨DC metro⟩",
"state": "VA",
"country": null
},
"api_skills": [
"Security",
"Information Security",
"Information Assurance",
"PKI",
"Computer Security",
"Integration",
"Encryption",
"Identity Management"
],
"api_industry": "Computer and Network Security",
"api_seniority": {
"totalExperienceYears": 33,
"currentTenureYears": 25,
"averageTenureYears": 6.4
},
"api_publications_count": 24,
"api_patents_count": 4,
"languages_count": 0,
"certifications_count": 3,
"skills_count": 50,
"outreach_ready": true
}kept_as + visa_route + criteria_met_count + the evidence arrays answer the first question — which attorney should even look at this — before anyone opens a résumé. A ranked cohort is a pre-sorted intake queue.
api_publications[], api_patents[], honorsAwards[], plus the education arc (foreign_universities[] → US graduate school) arrive titled and dated — the raw material of an exhibit list, assembled before the first call.
db_personal_emails[] (verified-or-empty), db_profile_activity_at (is the person actually active), and the self-written headline — enough to write a first line that references their real work, not a template.
experiences[] with employers and geographies, current_company registry data, and career timelines support conflict checks and jurisdiction questions from the record itself, not from what a prospect remembers to mention.
The complete field inventory, grouped the way the delivery README documents it. Related keys are bundled into one row where they travel together.
| Field | Type | What it is |
|---|---|---|
| profile_id · public_identifier | id | Stable identifiers for dedup and re-order ledgers |
| full_name · first_name · last_name | string | Identity as publicly stated |
| headline | string | The person's own one-line self-description — outreach gold |
| summary · api_about | text | Long-form self-description where present |
| connections · api_followersCount | number | Network scale — a soft prominence signal |
| api_profileImageUrl | url | Profile imagery for outreach personalisation |
| Field | Type | What it is |
|---|---|---|
| location_country_code | string | The person's country (ISO) — the mandatory person-level geo filter |
| location_name | string | City / metro as text |
| api_location | object | Structured city · state · country from enrichment |
| Field | Type | What it is |
|---|---|---|
| experiences[] | array | Every recorded position: title, employer, employer size, geography, start/end years, is_current |
| current_title | string | Current role as stated |
| positions_count | number | Total recorded positions — career graph depth |
| total_experience_years | number | Career length, computed from dated positions |
| us_experience_years | number | Years physically in US roles — the arc quantified |
| api_seniority | object | Tenure analytics: current-role tenure, average tenure |
| background_has_prestige · prestige_source | bool · string | Recognized-employer marker with its reason |
| Field | Type | What it is |
|---|---|---|
| education[] | array | Institutions, degrees, fields of study, years |
| has_foreign_education | bool | Non-US institution present on the record |
| foreign_universities[] | array | The non-US institutions, named |
| foreign_education_reason | string | Why the arc classifier fired — auditable, not a black box |
| Field | Type | What it is |
|---|---|---|
| current_company_name · _id · _domain | string | Employer identity |
| current_company_country | string | Registration geography — the F1 signal lives here |
| current_company_employee_count · _size_code | number | Headcount — separates operators from shells |
| current_company_founded | number | Company age |
| current_company_industry · db_company_description | string | What the company does |
| db_company_website · _follower_count · _parent_id | mixed | Web presence, audience, corporate-family link |
| Field | Type | What it is |
|---|---|---|
| api_publications[] | array | Titled publications with publisher and date |
| api_patents[] | array | Patent entries with numbers where stated |
| api_accomplishments.honorsAwards[] | array | Awards with issuer and date |
| patents_count · publications_count · awards_count | number | Per-class tallies used by ranking |
| api_certifications[] | array | Credentials with authority and dates |
| projects · organizations | array | Named projects and memberships |
| Field | Type | What it is |
|---|---|---|
| api_skills[] · skills_count | array | Declared skills |
| languages_count · api_accomplishments.languages | array | Languages with proficiency where stated |
| api_industry | string | Person-level industry classification |
| Field | Type | What it is |
|---|---|---|
| db_personal_emails[] | array | Verified personal emails from the multi-source waterfall |
| db_personal_numbers[] | array | Phone numbers where they exist |
| db_twitter_url · db_facebook_url · db_github_url | url | Public social and code-hosting profiles |
| api_contactInformation | object | Enrichment-side contact block, merged never collapsed |
| outreach_ready | bool | Delivery gate: contactable now |
| Field | Type | What it is |
|---|---|---|
| db_profile_updated_at | date | When the profile record last changed |
| db_profile_activity_at | date | Most recent observed public activity |
| api__enriched_at · api__source | date · string | When and how the enrichment layer was fetched |
| _db_augmented · has_api_enrichment | bool | Which layers this row carries — stated, not hidden |
| Field | Type | What it is |
|---|---|---|
| kept_as | string | The structural archetype that selected this person |
| visa_route | string | Route mapping — O-1A / E-2 / L-1 vs EB-1A / NIW vs declared |
| criteria_met_count | number | How many argued criteria the record supports |
| lead_score · final_rank · intent_score | number | Ranking within the cohort, with components documented |
| score_details[] | array | Which score components fired — the formula is inspectable |
| golden_kw[] · soft_kw[] · intent flags | array | Declared-intent markers, used to re-rank only |
| corrupt_name · merger_shell flags | bool | Quality-control flags from the audit pass |
The never-trim rule
The object ships whole. Graph fields, enrichment fields and computed fields are independent layers — we never collapse one into another, and we never delete a field to make a file smaller. What the pipeline saw is what you receive, and the README documents every field group above.
Describe your routes and geographies in two sentences; we answer with a 10-profile sample of full objects (contacts masked) drawn from the cohort that fits your practice best. AI outreach agents can draft the first touch straight from these fields — referenced to each person's actual career, so the mail lands with a real person, not a template.
Or just write — a consultation is free: we will map this cohort to your practice.
Outricher · Visa Talent Engine · the example object is a real masked pull · MEASURED · Outricher data