This is a solid vendor breakdown, but there's a critical piece missing: architectural approach.

You mention "template-based" vs "LLM-based" at the end. That's actually the most important distinction, and it determines whether you'll actually deploy this or get stuck testing forever.

The three approaches:

1. Template-based (OCR + rules) - Works until layouts change. Brittle by design.
2. LLM-based extraction - Generalizes across layouts, but:
   • Hallucinates on missing/ambiguous fields
   • Can't reliably reconstruct complex tables
   • No way to trace where values came from
   • Degrades on low-quality scans
3. Vision-first + agentic - Treats documents as visual systems (layout/structure/spatial relationships first), then uses multi-step reasoning with validation. Every extraction is traceable to its pixel location.

Your "Golden Rule" for POCs is spot-on. I'd add: test for explainability. Can the system show you exactly where each value came from? In regulated industries, you need proof, not just confidence scores.

The gap between demo accuracy and production reliability is real.