What is Upstage AI?

Upstage AI provides generative intelligence and document processing tools for enterprise workflows. Solar Pro 2, Solar Mini, and Syn Pro are LLMs designed for complex tasks, low-latency inference, and Japan-specific language needs. Document Parse converts PDFs, scanned images, and emails into clean, LLM-readable text and OCR outputs.

Information Extract pulls structured key-value data from invoices, claims, contracts, and other unstructured documents. AI Space offers centralized document search, question answering with source citations, and team collaboration for faster document review.

Deployment options include REST API integration, AWS Marketplace, and on-prem or hybrid installs for data sovereignty and compliance. Common use cases include insurance claims automation, clinical and operational document workflows, contract review, and financial due diligence.

Upstage AI pricing

Document classify $0
Document parse $0.01/pages
Information extract $0.04/pages
Enhanced $0.04/pages
Prebuilt receipt $0.05/pages
Solar pro 2 $0.15/mtok
Document ocr $0.0015/pages
Prebuilt logistics $0.15/pages
Commitment tiers $100+/mo prepaid
Save 20% with yearly billing $100+/yearly prepaid
Embed $0.10/1m tokens
Solar mini $0.15/1m tokens
Build $500+/mo prepaid
Explore $1200+/yearly prepaid
Scale $5000+/mo prepaid
Build $6000+/yearly prepaid
Scale $60000+/yearly prepaid

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Upstage AI's key features

  • Solar LLM family (Solar Pro 2, Solar Mini): enterprise-grade models optimized for speed and groundedness
  • Syn Pro: locally trained large language model for Japan
  • Document Parse: converts PDFs, scans, and emails into clean, machine-readable text for LLM pipelines
  • Information Extract: extracts structured key-value data from unstructured documents (invoices, claims, contracts)
  • AI Space: command center for querying and interacting with document collections

Upstage AI use cases

  • Create an automated invoice and insurance-claims processing pipeline by ingesting PDFs and images with OCR, extracting structured fields (amounts, dates, policy numbers), validating entries with low-latency LLM inference for near real-time approvals, and deploying on-premise or via AWS to meet compliance requirements in Japan
  • Develop a contract review and document QA system that centralizes PDFs and contracts into a searchable repository, uses Japan-specific LLMs to identify key clauses and risks, generates evidence-backed summaries and citations for auditors, and enables collaborative review workflows for legal and compliance teams
  • Implement a centralized knowledge base and customer-facing Q&A assistant that parses technical manuals, SOPs and support tickets via OCR and structured extraction, provides low-latency, citation-backed answers in Japanese and English, and runs via REST/AWS or on-prem deployment so teams can review, update, and govern responses

Who is it for?

  • Enterprise customers
  • Software developers
  • System administrators
  • Business analysts
  • Technical teams

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