Sample pack
Start with a bounded set of real pages, expected fields, edge cases, and redaction rules.
- Invoices and forms
- Scans and screenshots
- Expected output schema
Private document workflow offer note
Private document intake benchmark for local AI workflows
Want an AI to read your invoices, forms, screenshots, and PDFs? Run this benchmark before you send documents into a local AI workflow. You get a clear fit report: how accurate the reading is, where fields go wrong, how tables behave, privacy boundaries, and what fits an RTX 4000 Ada card.
- Representative pages and expected fields come before production promises
- Docling-style parsing and OCR fallbacks are tested alongside vision-language candidates
- Qwen3-VL 8B and Qwen2.5-VL 7B are benchmark candidates, not live-throughput claims
Updated 2026-05-31; live local-inference claims remain gated until the actual server passes driver, Ollama, and model smoke tests.
Parsing baseline
Run deterministic extraction and OCR/table baselines before asking a vision model to reason.
- Docling conversion
- OCR fallback
- Table structure notes
Business Secure
For production documents, intake belongs behind access control, audit scope, and change windows.
- RBAC scope
- Storage and retention rules
- Review gates
Benchmark gates before document automation
These gates turn document-AI interest into a paid, evidence-based setup instead of a brittle extraction demo.
Select real but bounded documents
Use invoices, forms, scanned PDFs, photos, and screenshots that represent the buyer workflow. Remove secrets and define which fields must be extracted.
Measure OCR and layout first
Run a deterministic parser and OCR/table baseline so the report can separate text extraction issues from model reasoning issues.
Define review and handoff rules
Decide which fields may be auto-filled, which need human review, and where logs, outputs, and source files may live.
Our promise: we run the benchmark and hand you the fit report on your own server before we claim any live document automation.
Model-fit checks for local document intelligence
Current vision-language and parsing projects make private document workflows attractive, but 20 GB VRAM still needs measured limits.
8B vision model candidate
Qwen3-VL 8B is a current candidate for OCR-heavy image understanding and long-document structure parsing. It still needs local framework, memory, and latency checks.
7B fallback candidate
Qwen2.5-VL 7B remains useful for text, charts, layouts, structured outputs, and form-style extraction tests where a smaller candidate may fit better.
Parser before model reasoning
Docling gives a local document-conversion baseline for PDFs, images, OCR, reading order, and table structure before a model is asked to interpret the result.
What the benchmark report should prove
The deliverable must be useful to a buyer even if a fully local live workflow is not ready on day one.
Can it load?
Record driver state, Ollama or framework health, model availability, context target, VRAM behavior, and startup failures.
Can it extract?
Compare extracted fields against expected values, including missing fields, hallucinated fields, and table or checkbox errors.
Can it hand off?
Define output formats, review queues, retry behavior, and integration points before automation touches production records.
Can a team run it?
Define access, logs, retention, backups, update windows, user count, and support boundaries.
Primary sources tracked
These sources guide scope language; final claims still depend on the server runtime and the buyer document set.
Qwen3-VL 8B
Use the official model card for OCR, visual reasoning, context, and implementation planning.
Hugging Face model card →Qwen2.5-VL 7B
Use the official release notes and model card for structured outputs, visual layouts, and fallback planning.
Qwen2.5-VL blog →Hugging Face 7B card →Local document conversion
Use Docling for a local parsing and OCR baseline before model reasoning is evaluated.
Docling documentation →RTX 4000 Ada 20 GB
Use NVIDIA specifications as the public hardware boundary, not as an automatic throughput promise.
NVIDIA RTX 4000 Ada →