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What You Can Build

The Xavigate Nature API v1 gives you a scored Multiple Natures (MN) and Multiple Intelligences (MI) profile for any person who completes the MNTEST assessment. Here's what that unlocks today — and what's coming next.

What's available now​

The Nature API is fully operational in closed beta. Every use case below works against live endpoints with your API key.

Coaching and facilitation tools​

Profile clients before sessions. After an assessment completes, your webhook receives the profile automatically — no polling required.

# Subject creation with consent artifact
curl -X POST https://api.xavigate.com/v1/nature/subjects \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "external_subject_id": "client_123",
    "consent": {
      "text_version": "1.0",
      "granted_at": "2026-04-22T10:00:00Z",
      "ui_hash": "sha256:abc123"
    }
  }'

Use the profile's top Nature and Intelligence scores to shape session focus. High educative + high linguistic? Different session direction than high entrepreneurial + high visual_spatial.

Team mapping​

Run profiles across a group, then compare Nature and Intelligence distributions on your side.

// Fetch profiles for multiple subjects
const profiles = await Promise.all(
  subjectIds.map(id =>
    client.nature.subjects.profile(id)
  )
);

// Compare top-3 Natures per person — ranking is your logic
const teamMap = profiles.map(p => ({
  id: p.subject_id,
  topNatures: Object.entries(p.natures)
    .sort(([, a], [, b]) => b - a)
    .slice(0, 3)
    .map(([name]) => name)
}));

Surface where a team's Natures cluster, where they diverge, and what's missing. Complementarity and gap analysis is developer-side — you decide what the patterns mean for your use case.

HR onboarding pipelines​

Profile new hires as part of structured onboarding. Use external_subject_id to link profiles to your internal employee IDs. Use metadata fields to tag cohort, role, or hire date.

curl -X POST .../v1/nature/subjects \
  -d '{
    "external_subject_id": "emp_456",
    "metadata": {
      "cohort": "2026-Q2",
      "role": "senior-engineer",
      "hire_date": "2026-04-15"
    },
    "consent": { ... }
  }'

Scores are stored against your subject. Query them later for cohort analysis, retention modeling, or role-fit review.

Personalized learning paths​

Branch content by top Intelligence scores. A learner high in visual_spatial gets different scaffolding than one high in linguistic or logical.

const profile = await client.nature.subjects.profile(subjectId);

const topIntelligence = Object.entries(profile.intelligences)
  .sort(([, a], [, b]) => b - a)[0][0];

// Branch your content delivery
const learningPath = LEARNING_PATHS[topIntelligence] ?? DEFAULT_PATH;

The 10 Intelligence scores (0.0–1.0) are always returned alphabetically. Ranking and branching is your responsibility.

Assessment-as-a-service​

White-label the MNTEST inside your own product. Your users complete the 76-item assessment; you receive the scored profile via webhook when they finish. Your users never need to know Xavigate is involved.

Flow:

  1. POST /v1/nature/subjects — create a subject for each user
  2. POST /v1/nature/subjects/{id}/assessments — start the assessment
  3. Present each of the 76 items to your user; collect responses
  4. PATCH /v1/nature/subjects/{id}/assessments/{id} — submit responses in batches as they come in
  5. Scoring triggers automatically when all 76 responses are received
  6. Your webhook endpoint receives nature.assessment.completed with the profile ID

Career exploration (partial)​

Use Nature scores as raw input to your own career matching logic. The full Careers API — 37,000 career entities matched against Nature and Intelligence profiles — ships approximately 3 weeks after the Nature API launch.

What the profile contains​

Every completed assessment returns:

9 Nature scores (0.0–1.0, alphabetical): administrative, adventurous, creative, educative, entertaining, entrepreneurial, healing, protective, providing

10 Intelligence scores (0.0–1.0, alphabetical): bodily_fine_motor, bodily_gross_motor, interpersonal, intrapersonal, linguistic, logical, musical, naturalistic, visual_graphic, visual_spatial

Narrative descriptions (English, v1): Rule-based, deterministic. Returned only for constructs with a raw score ≥ 7. Up to 3 Natures + 3 Intelligences. Single-sentence descriptions.

Scores are always returned alphabetically. Ranking — determining which Nature is "highest" or "most dominant" — is always developer-side logic.

What's coming next​

CapabilityVersionApproximate timing
Careers API — match any profile against 37,000 career entitiesv1.0.5~3 weeks post-Nature launch
French language support — FR assessment + FR narrativesv1.0.5same release as Careers
Situational API — LLM-augmented situation-alignment scoringv1.1Q2 2027
Content Generator API — kernel-verified content generation grounded in a person's profilev1.1Q2 2027
MCP serverv1.1Q2 2027 (high priority)
Multi-seat / RBAC dashboardv1.1Q2 2027
Closed beta

The Nature API is in closed beta. Reach out if you're building on it.

What you can't build yet​

These use cases require APIs that aren't available at v1:

  • Career matching — needs the Careers API (v1.0.5, ~3 weeks out)
  • Situation analysis — "how well does this person fit their current role?" — needs the Situational API (v1.1)
  • Profile-grounded content generation — needs the Content Generator API (v1.1)
  • French-language assessment flows — French accepted as a parameter but English is used at v1; FR ships in v1.0.5
  • Multi-user team accounts — single-account only at v1; RBAC in v1.1

Ready to start?​

→ Quickstart: get your first scored profile in under 3 minutes.