💰 FUNDING NEWS: Hushh.ai Secures $5 Million Strategic Investment from hushhTech.com's Evergreen Renaissance AI Fund

💰 FUNDING NEWS: Hushh.ai Secures $5 Million Strategic Investment from hushhTech.com's Evergreen Renaissance AI Fund

💰 FUNDING NEWS: Hushh.ai Secures $5 Million Strategic Investment from hushhTech.com's Evergreen Renaissance AI Fund

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Developers

Profile Data and Enrichment

Follow the full profile pipeline from consent through activation.

Profile Data and Enrichment

This page explains how brand teams build a consent-first profile pipeline using MuleSoft agentic APIs and optional Plaid signals.

The data pipeline

1) Consent and identity

Collect the minimum identifiers required for enrichment and request user consent.

2) Profile creation or query

Create a new profile or retrieve an existing one from Supabase.

3) Public and Gemini enrichment

Generate a complete profile with public data and high-confidence gap filling.

4) Brand preference normalization

Translate signals into likes, dislikes, and brand affinities for CRM activation.

5) Activation

Store the merged profile in your CRM, CDP, or personalization system.

MuleSoft flow

MuleSoft orchestrates the agent chain, applies retry logic, and normalizes responses through DataWeave before data is stored or activated.

Plaid signals (optional)

Plaid is used to enrich merchant and category signals that strengthen brand affinity modeling.

warning

Plaid calls must be server to server. Do not run Plaid endpoints from the browser.

Common Plaid endpoints in this flow:

  • POST /link/token/create
  • POST /item/public_token/exchange
  • POST /accounts/get
  • POST /transactions/sync
  • POST /identity/get (if identity is consented)

Map Plaid outputs into brand affinity fields before calling the Brand agent.

Testable endpoints

Supabase Profile Creation Agent

POST/api/a2a/hushh-profile
Create a Supabase profile

Supabase Profile Update Agent

POST/api/a2a/hushh-profile-update
Update an existing Supabase profile

Supabase Profile Query Agent

POST/api/a2a/hushh
Query a Supabase profile by phone or email

OpenAI Public Data Agent

POST/api/a2a/public
Public data enrichment

Gemini Public Data Agent

POST/api/a2a/gemini
Second-pass enrichment

Brand User Data Query Agent

POST/api/a2a/brand
Normalize likes, dislikes, and brand affinities

Quickstart prompts (paste into the Api Tester)

Use these prompts in the text field for the endpoints above.

Create a profile (Supabase Profile Creation Agent)

developer-docs-inline-code
Create a profile for Sundar Pichai with email sundar.pichai@example.com, phone +1 6505559001, occupation CEO, city Mountain View, and country USA.

Update a profile (Supabase Profile Update Agent)

developer-docs-inline-code
Update the city for Sundar Pichai to Mountain View and set the occupation to CEO.

Query an existing profile (Supabase Profile Query Agent)

developer-docs-inline-code
Fetch all details for Sundar Pichai with phone +1 6505559001.

Enrich with OpenAI Public Data Agent

developer-docs-inline-code
Provide a detailed JSON profile for Sundar Pichai, email sundar.pichai@example.com, phone +1 6505559001.

Enrich with Gemini Public Data Agent

developer-docs-inline-code
Using the Gemini Public Data Agent, provide a detailed JSON profile for Sundar Pichai with email sundar.pichai@example.com and phone +1 6505559001, including demographics, lifestyle, and intent fields.

Normalize brand affinities (Brand User Data Query Agent)

developer-docs-inline-code
Summarize likes, dislikes, and brand affinity for Sundar Pichai using the consented profile.

Example merged profile output

json
{
  "fullName": "Sundar Pichai",
  "email": "sundar.pichai@example.com",
  "phone": "+1 6505559001",
  "likes": ["fine leather goods", "limited drops", "Paris fashion week"],
  "dislikes": ["fast fashion", "discount-only messaging"],
  "brand_affinities": ["Dior", "Louis Vuitton", "Sephora"],
  "shopping_preferences": {
    "category_focus": ["handbags", "beauty", "fragrance"],
    "price_sensitivity": "low",
    "channel": "in-store and VIP events"
  },
  "confidence": 0.86
}