> For the complete documentation index, see [llms.txt](https://infronai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infronai.gitbook.io/docs/confidential-ai/overview.md).

# Overview

Use Confidential AI through an OpenAI-compatible API, with attestation reports and signed receipts you can verify.

### Why Confidential AI?

Traditional AI platform ask you to trust the platform operator and the upstream model provider. Infron Confidential AI runs inference through an attested gateway and verified TEE providers, then gives you cryptographic evidence for each response.

The on-demand API is OpenAI-compatible and served through an Attested Confidential Inference (ACI) gateway. Every response includes a signed receipt, and the gateway publishes an attestation report that proves which TEE workload served the request. Confidential model responses additionally show, in the receipt, that the upstream provider was verified and channel-bound before your prompt was forwarded.

 Infron offers pre-deploy serverless **Models** through an API for quick integration. See [available models](https://infron.ai/models) for supported models, use cases.

### Quick Tour of Confidential AI

#### Models: API Access

**API Access** provides pre-deployed models through an OpenAI-compatible API at `https://llm.onerouter.pro/v1`. Pay per request with no infrastructure to manage. Start with [API Access](/docs/overview/quickstart.md).

For advanced API features, explore [Tool Calling](/docs/features/tool-calling.md) to enable LLMs to interact with external tools and APIs securely within [TEE](/docs/confidential-ai/verification/overview.md).

#### Make Your First Request

Replace `<API_KEY>` with your actual API key. The examples below use [`z-ai/glm-5.2`](https://infron.ai/models/z-ai/glm-5.2); use [List Models](/docs/models-and-providers-apis/models-api/list-all-models-and-their-properties.md) to choose a model for your workload.

{% tabs %}
{% tab title="cURL" %}

```bash
curl https://llm.onerouter.pro/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
  "model": "z-ai/glm-5.2",
  "messages": [
    {
      "role": "user",
      "content": "What is the meaning of life?"
    }
  ]
}'
```

{% endtab %}

{% tab title="Python" %}

```python
import requests
import json

response = requests.post(
  url="https://llm.onerouter.pro/v1/chat/completions",
  headers={
    "Authorization": "Bearer <API_KEY>",
    "Content-Type": "application/json"
  },
  data=json.dumps({
    "model": "z-ai/glm-5.2", 
    "messages": [
      {
        "role": "user",
        "content": "What is the meaning of life?"
      }
    ]
  })
)
print(response.json()["choices"][0]["message"]["content"])
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
fetch('https://llm.onerouter.pro/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: 'Bearer <API_KEY>',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'z-ai/glm-5.2',
    messages: [
      {
        role: 'user',
        content: 'What is the meaning of life?',
      },
    ],
  }),
});
```

{% endtab %}
{% endtabs %}

The response is a standard OpenAI chat completion. In raw responses, Infron also returns verification items:

| Item                | Meaning                                                                |
| ------------------- | ---------------------------------------------------------------------- |
| `receipt-id`        | Receipt id for this response. Use it with `GET /v1/aci/receipts/{id}`. |
| `aci-identity`      | Attested gateway workload identity.                                    |
| `aci-keyset-digest` | Digest of the gateway keyset used for receipt verification.            |

#### Verify Attestation and Receipts

To verify the API path, [fetch a fresh Attestation Report](/docs/confidential-ai/verification/attestation-report.md) from `GET /v1/aci/attestation`. It proves the gateway workload identity, TEE quote, source provenance, and public keyset used to sign receipts.

Then use the response `receipt-id` item to [fetch the Receipt](/docs/confidential-ai/verification/get-receipt.md). The receipt binds request and response hashes to the attested workload and records whether the upstream provider was verified. See [Verify a Response](/docs/confidential-ai/verification/verify-a-response.md) for the full flow.


---

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