Document Q&A / PDF Chat With an API
As of July 2026, AIDiveForge tracks 7 document q&a / pdf chat with an api. Curated document q&a / pdf chat with an api tracked by AIDiveForge. Listings are verified against each tool's live website and re-checked regularly.
Last updated July 7, 2026 · 7 tools
1. AI2JSON
The tool accepts a PDF over a single REST call and returns typed JSON with extracted fields, line items, dates, amounts, and a confidence score — no field mapping, no OCR configuration. The vendor states 95%+ accuracy on common document types like invoices and receipts, with average parse times under two seconds. OCR for scanned or image-based PDFs is a paid-only feature, so teams processing physical document scans on the free tier will hit a wall immediately. Volume limits are strict: the free tier covers 10 documents per month, and batch processing is a paid-only feature — meaning teams with high-volume pipelines need to plan their tier before going to production.
Paid
2. Bol.ai
Upload a PDF, scan, or phone photo of a Bill of Lading and the tool returns a structured JSON or CSV payload covering 20+ fields — B/L number, parties, ports, containers, weights, Incoterms — in seconds. Every container number is checked against its ISO 6346 check digit; dates and weights run through plausibility rules; suspect fields are flagged rather than silently passed through. Drop in a matching commercial invoice and packing list and Bol.ai links all three documents, surfacing mismatches before they reach customs. The API and webhook outputs mean the extracted data can land directly in a TMS, ERP, or declaration workflow without a manual export step.
PaidFree Trial · 7 days
3. ContextOCR.dev
ContextOCR converts scanned documents, PDFs, and email attachments into structured Markdown, preserving page layout, table geometry, and barcode data so downstream AI agents receive context they can actually use. The vendor states the API handles barcodes decoded directly from forms and labels — a capability most general-purpose OCR skips entirely. The credit-based billing model means a low-volume proof of concept costs almost nothing, but teams indexing tens of thousands of documents per month will hit real costs fast and need to model that before committing. There is no self-hosted option, which means every document you process leaves your infrastructure.
Paid
4. Docubix
The core workflow is upload, configure, integrate: drop in PDFs, DOCX, TXT, or Markdown files, write a system prompt, tune retrieval settings, and call a single REST endpoint from your app. Every answer surfaces the exact document and page it pulled from, so users can verify rather than trust. The free tier caps at one knowledge base, 20 documents, and 100 queries per month — enough to validate a use case, not enough to run a real support operation. Teams that outgrow those limits move to the paid tier, and teams that need multiple knowledge bases on free hit that wall immediately. There is no self-hosted option, so your documents live on Docubix infrastructure regardless of your compliance posture.
Paid
5. NinjaDoc Ai
Ninjadoc extracts structured JSON from PDFs and returns each field with a citation back to its source location in the original document, so every piece of data carries traceable proof. It is designed to be called from AI agent frameworks — including Claude and Cursor via MCP — which means it slots into agent pipelines without a custom wrapper. The extraction accuracy claim is built around this sourcing model: rather than summarizing, it anchors output to specific document regions. The ceiling appears when documents fall outside the structured PDF category — scanned images with low fidelity, handwritten forms, or multi-document comparison workflows push against what a single-API extraction service can handle. Teams needing cross-document reasoning or on-premises deployment hit the wall early.
Paid
6. ParseHawk
ParseHawk takes PDFs, scans, images, plain text, and Markdown and outputs structured JSON against a schema you define, entirely locally. The vendor describes support for zero-shot and few-shot extraction, which means you can describe what fields you want without building a labeled training set first. The API, CLI, and Web UI surface the same underlying model, so you can wire it into a batch pipeline or hand it to a non-engineer for one-off jobs. The ceiling appears when documents get structurally unusual — community reports suggest edge-case layouts and multi-page tables require prompt iteration that adds real engineering time. Teams processing genuinely complex documents often end up maintaining a library of per-document-type schemas.
FreeOpen Source
7. Umi-OCR
The tool handles screenshot capture, bulk image import, PDF extraction, and QR scanning through a GUI, a CLI, or an HTTP interface — all offline. Bundled OCR engines cover Chinese, Japanese, and other languages without additional downloads. Batch jobs on scanned archives run without throttling because there is no rate limit to hit. The ceiling appears when your documents need handwriting recognition or layout analysis that goes beyond what the bundled engines support — at that point you are looking at a custom engine swap, which the build docs describe but requires developer effort. Teams needing cloud-scale parallel processing across distributed workers will find the single-machine model too constrained.
FreeOpen Source
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