Previously, I was building internal tools for a fintech startup. We tried using GPT-4 Vision and various OCR APIs to automate our Accounts Payable. They worked great for ~90% of documents.

The problem was the other 10%: crumpled receipts, handwritten delivery notes, or invoices with weird layouts. In fintech, a 90% success rate isn’t automation; it’s a liability. We spent more time fixing the AI’s hallucinations than if we had just typed it manually.

I realized that for high-stakes operations, we didn’t need "better AI"—we needed a Safety Layer.

So I built SyncAI. It’s an extraction API that guarantees "Golden Records" (verified data) by routing uncertainty to humans.

How it works under the hood:

Ingestion: You send a PDF/Image via API.

Ensemble Extraction: We run it through a mix of OCR and LLMs to extract key fields.

Confidence Scoring: We calculate a confidence score for each specific field (not just the whole doc).

The Routing Logic:

If Confidence > 99% (and passes regex validation) → Auto-Approve (Returns JSON instantly).

If Confidence < 99% → Routes to HITL (Human-in-the-Loop) queue.

Verification: A human verifies only the low-confidence fields in a split-screen UI.

Callback: Your webhook receives the strictly typed, verified JSON.

We are positioning this for developers building Autonomous Finance Agents who need deterministic inputs.

I’ve set up a playground where you can try it without signing up. Upload your messiest invoice/receipt to see if we can parse it: https://sync-ai-11fj.vercel.app/

Pricing is usage-based (per doc). The documentation is here: https://sync-ai-11fj.vercel.app/docs.

I’d love to hear your feedback on the routing architecture or how you’re currently handling "AI hallucinations" in production pipelines.