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Scenario

Team wants structured user profiles for analytics, routing, or other internal logic — not just for LLM prompts.

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Extraction

They stream conversation logs, events, and reviews to GetProfile:
  • It extracts traits like:
    • NPS_risk: likely detractor vs promoter
    • churn_risk_reasons[]
    • product_feature_needs[]
    • expertise / segment (SMB vs enterprise, hobbyist vs pro)

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Injection

  • Some teams might still have their own LLM stack; they just call GET /profiles/:id to build prompts manually.
  • Others may later switch to GetProfile proxy to get automatic injection with the same traits.

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Impact

  • They can use traits everywhere:
    • marketing segmentation,
    • feature flags (“show advanced UI for experts”),
    • routing to different flows based on segment or risk.
  • And, if/when they want, they get “free” prompt injection by pointing their LLM client to the GetProfile proxy.

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Implementation

// Option 1: Extract-only (using SDK)
import { GetProfileClient } from '@getprofile/sdk';

const client = new GetProfileClient({
apiKey: process.env.GETPROFILE_API_KEY,
baseURL: 'https://api.yourserver.com',
});

// Stream events to extract traits
await client.memories.create({
profileId: userId,
content: 'User canceled subscription, mentioned pricing concerns',
metadata: {
event_type: 'churn_event',
},
});

// Later, retrieve profile for analytics
const profile = await client.profiles.get(userId);
const traits = await client.traits.list(userId);

// Use traits for routing, feature flags, etc.
if (traits.find(t => t.name === 'segment' && t.value === 'enterprise')) {
// Show enterprise features
}

// Option 2: Extract + Inject (using proxy)
import OpenAI from 'openai';

const llmClient = new OpenAI({
apiKey: process.env.GETPROFILE_API_KEY,
baseURL: 'https://api.yourserver.com/v1',
defaultHeaders: {
'X-GetProfile-Id': userId,
'X-Upstream-Key': process.env.OPENAI_API_KEY,
},
});

// LLM calls automatically get profile injection
const response = await llmClient.chat.completions.create({
model: 'gpt-5',
messages: [{ role: 'user', content: 'Help me with...' }],
});

​
Trait Schema Example

{
  "NPS_risk": {
    "type": "string",
    "enum": ["promoter", "passive", "detractor"],
    "description": "Likely Net Promoter Score category"
  },
  "churn_risk": {
    "type": "string",
    "enum": ["low", "medium", "high", "critical"],
    "description": "Risk level of user churning"
  },
  "churn_risk_reasons": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Factors contributing to churn risk"
  },
  "product_feature_needs": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Features the user has expressed interest in or needs"
  },
  "expertise": {
    "type": "string",
    "enum": ["beginner", "intermediate", "advanced", "expert"],
    "description": "User's expertise level"
  },
  "segment": {
    "type": "string",
    "enum": ["hobbyist", "smb", "mid-market", "enterprise"],
    "description": "User segment classification"
  }
}

​
Use Cases for Extracted Profiles

Marketing Segmentation

Route users to different marketing campaigns based on extracted traits

Feature Flags

Show/hide features based on user expertise or segment

Support Routing

Route support tickets to appropriate teams based on user profile

Analytics

Build dashboards and reports from structured profile data

SDK Integration

Programmatic access to profiles and traits

Profiles API

Retrieve profiles for analytics and routing

Export API

Export profile data for external analytics tools

Proxy Integration

Add automatic injection when ready