GreenNode, the Vietnamese company behind the GreenNode IDP document processing system, says the product has saved more than 2,000 workdays at a single bank and cut insurance claim review time on one platform from roughly 30 minutes to about five minutes. GreenNode IDP, built by Vietnamese engineers, combines OCR (optical character recognition, which converts document images to machine-readable text) with large language models (LLMs) to handle both printed and handwritten Vietnamese forms. The product was recognized at the Vietnam Digital Transformation Awards 2026 on October 14, according to VnExpress (published October 16, 2026).
What problem does standard OCR miss in document processing?
OCR tools convert scanned images to text, but they cannot interpret what that text means. In Vietnamese enterprises, loan files, medical records, and insurance claims often include handwritten sections. Staff must read each document, key the data into software, then cross-check it against related papers, a process that is slow and error-prone at scale.
Mai Hai Duong, Director of AI Product Development at GreenNode, told VnExpress (October 16, 2026) that for these tasks a tool needs to not only read characters but understand what those characters mean.
GreenNode IDP layers LLMs and VLMs (vision-language models, which process both the visual layout and textual content of a document) on top of OCR. The system can also separate signatures and stamps overlaid on text, and flag anomalies within the same document set.
How does the four-step document processing workflow operate?
According to VnExpress (October 16, 2026), each document set moves through four stages before reaching staff:
- Document classification: sorting incoming papers by type
- Field extraction: pulling specific data points from each document
- Cross-referencing: checking data consistency across documents in the same set
- Risk scoring and normalization: flagging anomalies and formatting output to the client’s required data structure
GreenNode also offers a no-code (no programming required) drag-and-drop interface for configuring new form types, and connects to existing business systems via API (application programming interface, a standard method for software to exchange data), reducing duplicate data entry.
What accuracy rates has GreenNode IDP reported?
In company-published tests, field-level accuracy reaches 99% for printed text and 94% for handwritten Vietnamese, as reported by VnExpress (October 16, 2026). GreenNode said these figures were verified in a live deployment at a company specializing in health insurance claims processing in Vietnam.
Mai Hai Duong acknowledged that 100% accuracy remains a challenge for any solution. At 94% handwriting accuracy, roughly five to six fields in every hundred may need human review. In GreenNode’s framing, the differentiator is the system’s ability to identify which specific fields fall below a confidence threshold and route only those to staff for verification.
What do the reported deployments show?
GreenNode shared two deployment figures with VnExpress (October 16, 2026):
- Banking: more than 300,000 document sets digitized and classified at a bank, saving more than 2,000 workdays
- Insurance: an insurance platform reportedly using GreenNode IDP processes more than 700,000 document sets per year; the company reports processing time under 30 seconds per set and claim review time down from roughly 30 minutes to roughly five minutes
Why do global tools leave a gap for Vietnamese document AI?
Widely used international tools such as Amazon Textract and Azure Read do not support Vietnamese, according to GreenNode’s assessment cited by VnExpress (October 16, 2026). A GreenNode representative said that rather than competing across every AI task, the company focuses on Vietnamese language, local form layouts, and domestic business workflows, where a local platform has a structural advantage over global alternatives.
For organizations handling sensitive data, GreenNode says IDP can be deployed on-premise (installed on the client’s own servers), on a private cloud, or on a public cloud with infrastructure based in Vietnam.
Before adopting document processing AI: what to check
- Does the vendor report field-level accuracy separately for printed text and handwriting, not just an overall document accuracy figure?
- Is Vietnamese handwriting explicitly supported, not just Vietnamese printed text?
- Is accuracy data validated in a comparable client deployment, not only internal benchmarks?
- Can the system flag low-confidence fields and route only those to human review, rather than passing all errors downstream?
- What deployment options exist: on-premise, private cloud, or public cloud with local data residency?
- Does the API connect to your existing business software without requiring custom middleware (custom-built connector code)?
- Can new form types be configured without writing code?
What this means for AI-search visibility
In Hingewise’s view, specific numbers are more likely than vague superlatives to get a product cited by AI.
When a procurement manager or IT director asks ChatGPT or Perplexity which AI tool can process Vietnamese handwritten forms?, the AI draws on sources with clear, verifiable answers. GreenNode’s published figures, such as under 30 seconds per set, more than 700,000 sets a year, and claim review cut from about 30 minutes to about five, are the kind of data points AI systems can extract, attribute, and repeat in generated answers. Vague phrases about leading accuracy are not.
GreenNode’s narrow positioning, Vietnamese language plus local workflows, is also a defensible niche in AI-generated responses. An AI recommending document processing tools for Vietnamese enterprises may favor a candidate that owns the most specific, documented answer to that question, while broad vendors competing on every front may be harder to recommend for any single use case.
In Hingewise’s view, the credibility of GreenNode’s accuracy claims depends on what gets published publicly. Self-reported benchmarks on proprietary test sets carry limited weight with both sophisticated buyers and AI citation systems. The company’s practice of verifying figures in client deployments is a step in the right direction; publishing those client-validated results in accessible, structured formats would raise the citability of this data significantly.
The variable worth watching is whether GreenNode’s public documentation, technical specs, and deployment case studies are structured in a way that AI systems can readily index. A well-performing product with poorly indexed supporting content loses ground to a comparable product with clear, structured, publicly accessible evidence. That gap is often where AI-search visibility is won or lost.
Source: VnExpress KHCN, October 16, 2026
Lam Nguyen · Hingewise
This article was drafted with AI assistance from the sources cited above and checked against them before publishing. The "What this means for AI-search visibility" section is Hingewise's editorial analysis, built on the frameworks and positions we use in our GEO work. How we work: Editorial policy.