Layout‑preserving document structuring for complex forms

The Document AI engine retains the spatial relationships of text blocks, tables, and images. This means a scanned contract with multi‑column layouts, native electronic forms, or PDFs with embedded tables will be converted into structured JSON without the usual scrambled table of contents that plague simpler OCR tools.

Business outcome: Extraction accuracy on table‑heavy documents improves dramatically, reducing manual re‑keying by 80 % or more in practice.

Asian‑language recognition with minimal pre‑processing

Upstage delivers native optical character recognition optimised for Korean, Japanese, and Chinese, without the need for additional language packs or third‑party add‑ons. It handles mixed‑script documents, handwritten annotations, and low‑quality scans better than generic English‑centric OCR services.

Business outcome: Teams expanding into East Asian markets can automate document intake and compliance checks without building a separate language‑processing pipeline.

Key‑information extraction powered by small‑LLM context

Beyond simple field detection, the system uses Solar models to understand the meaning and relationships of extracted entities. For example, it can identify a contract party’s name and link it to the correct signature block, or differentiate a billing address from a shipping address, even when labels are ambiguous.

Business outcome: Downstream claim processing or invoice management workflows become more reliable, with fewer false positives and higher straight‑through processing rates.

Solar models fine‑tuned for enterprise‑grade security and cost

The Solar family includes compact models (Solar‑Mini, Solar‑Pro, Solar‑Ultra) that run efficiently on private servers. They deliver strong performance on Korean and general knowledge tasks while being easy to fine‑tune on proprietary data, making them suitable for sensitive use cases such as internal Q&A and document summarisation.

Business outcome: Organisations can deploy generative AI capabilities without sending data to external APIs, meeting strict data residency and compliance requirements.

Developer‑friendly integration for real‑time and batch processing

Both the Document AI and Solar LLM services expose REST APIs and SDKs for Python, with detailed documentation. Engineering teams can embed extraction pipelines directly into existing apps, ERPs, or RPA bots without adopting a new low‑code platform.

Business outcome: The time from proof‑of‑concept to production can be measured in days rather than months for a skilled development team.

Air‑gapped and private‑cloud deployment for regulated sectors

Upstage supports on‑premise deployments of both the Document AI stack and Solar LLMs via Docker or Kubernetes, suitable for finance, healthcare, and government organisations that cannot use public cloud services.

Business outcome: Security‑sensitive businesses gain access to modern AI while retaining full control over their infrastructure and data sovereignty.

• Asian‑language document support, especially Korean and JapaneseNo other enterprise Document AI platform delivers the same level of accuracy on complex East‑Asian layouts and mixed‑script documents.
• Compact, deploy‑anywhere language modelsSolar models enable generative AI in private data centres, solving the toughest compliance challenges.
• Strong layout retention for tables and formsThe parser maintains structure, making extracted data directly usable for analytics or database ingestion.
• Developer‑centric documentation and SDKsIntegration is straightforward for teams already using Python and REST APIs, reducing procurement friction.

• End‑user tooling is minimalThe platform does not offer a drag‑and‑drop interface for business users; document automation requires developer effort.
• Ecosystem smaller than hyperscalersSystem integrators and community support are less widespread than for AWS, Azure, or Google Cloud AI services.
• Pricing opacity for smaller teamsCustom enterprise pricing means startups or SMBs cannot easily budget without engaging the sales team.
• Professional RealityUpstage is a power tool, not a polished product for non‑technical staff — if your organisation lacks embedded engineering talent, you’ll underutilise the platform’s capabilities and may be better served by low‑code alternatives like <a href='https://theaitoolsbox.com/tool/uipath-document-understanding-review/' style='color:#4F46E5;font-weight:700;text-decoration:none;'>UiPath Document Understanding</a>.

Automated insurance claims processing in South Korea

Insurers can ingest scanned claim forms, extract policy numbers and damage descriptions, and route them to the right adjuster. The Solar LLM can also summarise lengthy reports, all while keeping sensitive medical data on‑premise.

Private‑cloud internal knowledge base Q&A

A pharmaceutical company deploys Solar‑Pro internally to answer employee questions about SOPs and compliance documents. The model is fine‑tuned on internal R&D knowledge without leaking data to a public API.

Invoice automation for a multinational logistics firm

The Document AI API parses thousands of supplier invoices daily, identifies line items and tax fields, and pushes structured data directly into the ERP. Asian‑language invoices that previously required manual translation now process automatically.

Regulatory compliance document analysis in Japan

A financial services company uses the layout‑preserving extraction to convert contracts and regulatory filings into machine‑readable data, enabling automated risk checks and audit trails without losing context.

Sign up for a free Upstage developer account to obtain API credentials (no credit card required for initial testing).

Upload a sample document via the Document AI API or web playground to evaluate layout parsing and extraction accuracy on your specific format.

Integrate the API endpoints into your existing Python pipeline or middleware using the official SDK and documentation.

For on‑premise Solar LLM deployment, request a technical evaluation licence and work with Upstage’s solutions team to size the GPU environment and fine‑tune the model on proprietary data.