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Scenario

SaaS product with an in-app / website support chatbot that needs to remember user context across conversations.

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Extraction

From chats, tickets, and events, GetProfile keeps a live profile per end-user:
  • plan_tier: free / pro / enterprise
  • product_areas_used[]: “billing”, “integrations”, “dashboards”
  • recurring_issues[]: “confused about usage limits”, “OAuth failures”
  • frustration_level: low / medium / high
  • tone_preference: patient explanations vs short answers
No one fills this out manually; it’s inferred from conversations + app events.

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Injection

When the user talks to the bot again, calls go through the GetProfile proxy:
  • It injects a short profile block into the system prompt:
    • “Pro-tier user, power user of integrations, recently frustrated about rate limits, prefers direct answers.”
  • It also injects a couple of recent, high-signal memories:
    • last failed integration attempt,
    • last time support promised a fix.

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Impact

The bot can:
  • Skip basic docs if user is a power user.
  • Preempt “we’ve had this issue before” with context.
  • Use calmer, more careful language if frustration_level is high.

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Implementation

import OpenAI from 'openai';

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

// Support bot conversation
const response = await client.chat.completions.create({
model: 'gpt-5',
messages: [
{
role: 'system',
content: 'You are a helpful support agent. Be concise and technical for power users.',
},
{
role: 'user',
content: 'I keep getting rate limit errors on my API calls.',
},
],
});
// GetProfile automatically injects user profile and relevant memories

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Trait Schema Example

Proxy Integration

Learn how to set up the proxy for automatic injection

Trait Schemas

Configure custom traits for your support use case