Chainlink Confidential AI Attester
Runs LLM inference inside Trusted Execution Environment (TEE). Documents go in, LLM analysis comes out — the raw documents are never stored or exposed.
Beta product for the EthGlobal NYC hackathon. Get an API key at the Chainlink booth or via the #partner-chainlink channel in the EthGlobal Discord.
Playground UI: https://confidential-ai-dev-preview.cldev.cloud/playground — easiest way to try it. Everything there maps 1:1 to the API calls below.
Workflow 1 — Submit: POST /v1/inference
Auth: Authorization: Bearer $API_KEY — always use an env var, never hardcode.
Request shape:
{
"model": "gemma4",
"system_prompt": "",
"prompt": "...",
"resources": [{ "filename": "doc.pdf", "content_type": "application/pdf", "content_base64": "<base64>" }],
"cre_callback": { "url": "https://..." }
}
cre_callback is optional — omit it and poll instead.- Models:
gemma4 (images/general, default), qwen3.6 (long text).
- Prefer PNG over PDF for demos — PDF preprocessing can take up to 5 minutes.
Response: 202 Accepted → { "id": "...", "status": "queued" } — save the id.
For curl examples and multi-language snippets → [references/code-examples.md](references/code-examples.md) For full request/response spec, error codes, resource types → [references/api-reference.md](references/api-reference.md)
Workflow 2 — Poll: GET /v1/inference/{id}
Poll every 2–5 s until status is completed or failed.
Key fields on completion: output (LLM text), usage, completed_at.
For error symptoms → [references/troubleshooting.md](references/troubleshooting.md)
Writing Prompts That Work
Always enforce JSON output with two layers:
- System prompt — keep the default unless you have a specific reason to change it.
- User prompt — binary question + exact JSON schema to return
For per-use-case prompt templates (lending, KYC, accredited investor, proof of reserves) → [references/prompts.md](references/prompts.md)
