> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getprofile.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Internal Knowledge Assistant Tailored to Each Employee

> Company AI assistants that adapt to each employee's role and expertise

## Scenario

Internal "Ask AI" for company docs/code/processes that needs to adapt to each employee's role and knowledge level.

## Extraction

From questions, docs visited, and tools used, GetProfile creates:

* `department`, `team`, `role`
* `project_contexts[]`: which repos / services they touch
* `topic_familiarity`: "deep in payments service", "novice in infra", etc.
* `documentation_style_preference`: likes code samples vs concept docs

All inferred from their questions and link click patterns.

## Injection

When they ask, "How do I add a new metric to our billing pipeline?":

* GetProfile injects:
  * "User is on the billing team, familiar with service X but not Y; prefers answers with code snippets and direct links to runbooks."

* Plus a couple of relevant memories:
  * previous similar question and answer,
  * docs they read last time.

## Impact

* The assistant answers at the right depth and with the right references.
* Onboarding is smoother, since the assistant adapts to each newcomer over time.

## Implementation

<CodeGroup>
  ```typescript TypeScript theme={null}
  import OpenAI from 'openai';

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

  // Knowledge base query
  const response = await client.chat.completions.create({
  model: 'gpt-5',
  messages: [
  {
  role: 'system',
  content: 'You are an internal knowledge assistant. Provide answers at the appropriate depth for the employee\'s role and expertise.',
  },
  {
  role: 'user',
  content: 'How do I add a new metric to our billing pipeline?',
  },
  ],
  });
  // GetProfile injects employee's role, expertise areas, and documentation preferences

  ```
</CodeGroup>

## Trait Schema Example

```json theme={null}
{
  "department": {
    "type": "string",
    "description": "Employee's department"
  },
  "team": {
    "type": "string",
    "description": "Employee's team"
  },
  "role": {
    "type": "string",
    "description": "Job title or role"
  },
  "project_contexts": {
    "type": "array",
    "items": {
      "type": "string"
    },
    "description": "Repositories, services, or projects the employee works on"
  },
  "topic_familiarity": {
    "type": "object",
    "description": "Familiarity level per topic or service",
    "additionalProperties": {
      "type": "string",
      "enum": ["novice", "intermediate", "expert"]
    }
  },
  "documentation_style_preference": {
    "type": "string",
    "enum": ["code-samples", "concept-docs", "step-by-step", "reference"],
    "description": "Preferred documentation format"
  }
}
```

## Related Resources

<CardGroup cols={2}>
  <Card title="Proxy Integration" icon="arrow-right-arrow-left" href="/openai-compatibility">
    Set up automatic context injection for your knowledge base
  </Card>

  <Card title="Memories API" icon="brain" href="/api-reference/memories/create">
    Store and retrieve past questions and answers
  </Card>
</CardGroup>