verifydoc

bhaskargurram-ai/verifydoc

★ 2 stars Python AI/LLM Updated today

Trust layer for document→JSON extraction & AI agents: calibrated per-field confidence + source grounding + accept/review abstention on any OCR/VLM. Ships VerifyDocBench, a novel grounding-conditioned conformal method, and an MCP server.

View on GitHub → 🔍 Audit Wallet Slippage →

Quick Install

Copy the config for your editor. Some servers may need additional setup — check the README.

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "verifydoc": {
      "command": "uvx",
      "args": [
        "verifydoc"
      ]
    }
  }
}

Or install with pip: pip install verifydoc

README Excerpt

> **The trust layer for document → structured-JSON extraction.** Wrap any extractor — get back JSON where **every field** carries a **calibrated confidence**, a **source grounding** (page + bbox / char span), and an **accept/review decision** tuned to your error budget. *Above: a real pipeline run (`scripts/make_demo_gif.py`). The extractor returned `$1,432.50`; the page says `$1,234.50`. Grounding support drops to 0.78, the field misses the accept threshold, and the reviewer is pointed at the e

Topics

agentic-aiai-agentsbenchmarkcalibrationconformal-predictiondocument-aidocument-extractionhallucination-detectionllmmcpmodel-context-protocolocrselective-predictiontrustworthy-ai

verifydoc

Quick Install

README Excerpt

Topics

Related AI/LLM Servers