AwaitVerify

result = await verify_document(
    document_path="invoice.pdf",
    task_description="Extract line items and totals.",
    response_schema=Invoice,
    prior_extraction=Invoice(...),    # optional: your initial guess
)
# result is an Invoice instance with the fields the reviewer confirmed/corrected.

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Who AwaitVerify is for

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What makes it different from “just call an LLM”

1. A real human is in the loop. Founder pool today, then platform reviewers as we scale. The human sees the document and the extraction side by side and corrects the cells that the model got wrong.
2. Typed end-to-end. You hand us a Pydantic model. You get back an instance of that model. No JSON twiddling, no string parsing, no “did GPT hallucinate this field” doubt.
3. Document never leaves your machine intact. The SDK fragments the document into masked views client-side and encrypts each fragment with AES-256-GCM before upload. The reviewer sees five partial views per page; the full document is reconstructable only inside your Python process. Security details →
4. You pay per page reviewed, not per request. $0.80 standard,$1.60 Express. Failed tasks aren’t billed. Pricing →

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Quickstart

pip install "awaithumans[awaitverify]"

import os
from awaithumans import verify_document
from pydantic import BaseModel

os.environ["AWAITHUMANS_API_KEY"] = "ah_sk_live_..."

class Receipt(BaseModel):
    vendor: str
    total_cents: int

result = await verify_document(
    document_path="receipt.png",
    task_description="Confirm vendor name and total in cents.",
    response_schema=Receipt,
)
print(result.vendor, result.total_cents)

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Multi-page documents

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API key

from awaithumans import AwaitHumans

client = AwaitHumans(api_key="ah_sk_live_...")
result = await client.verify_document(...)

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Where to go next