Government Document Compression - AI Data Preprocessing Solution

Official documents with red headers, scanned copies, and attachment materials have large file sizes. The accumulation of massive historical archives causes rapid expansion of storage space, and hardware expansion costs remain high.

OFD and PDF are used in parallel with various versions. Embedded images, fonts, and vector graphics create complex resource structures that traditional compression tools struggle to handle effectively, resulting in unstable compression ratios.

Xinchuang substitution requires full-stack adaptation of software and hardware, from operating systems and CPUs to databases and middleware. Compression solutions must run stably in domestic environments.

Government electronic records must be preserved long-term, with archiving periods lasting decades. Uncompressed original files continuously consume storage resources, bringing ongoing expansion and maintenance costs.

Intelligently identifies the internal structure of official documents, performing targeted optimization on embedded images, vector graphics, and redundant fonts, significantly reducing file size without affecting layout and readability.

Supports the national cryptographic SM2/SM3/SM4 algorithm suite. Compressed documents can be encrypted, signed, and integrity-verified, meeting the cryptographic application compliance requirements of government information systems.

Completed adaptation verification for mainstream Xinchuang software and hardware stacks, running stably on Kylin/UOS operating systems, Phytium/Kunpeng CPUs, Dameng/Kingbase databases, and TongWeb middleware environments.

Structured recording of metadata for each compression operation. Log entries are linked through tamper-proof mechanisms, and any tampering will cause verification failure, meeting government audit compliance traceability requirements.

Receive documents for compression through standardized REST API, supporting batch task submission and asynchronous callbacks, seamlessly integrating with existing electronic document systems.

Automatically identifies OFD, PDF, and their internal resource structures, distinguishing text, images, vector graphics, and other types to provide a basis for subsequent compression strategy selection.

Automatically matches optimal compression strategies based on file type and content characteristics, performing image resampling, font subsetting, and redundant resource cleanup to maximize compression ratio while preserving layout.

After compression, output files are signed with national cryptographic SM2 and integrity-verified with SM3, ensuring documents are not tampered with throughout the compression, transmission, and archiving pipeline.

Compressed and signed documents are written back to the electronic archive system or object storage, significantly reducing archive storage footprint, with audit logs retained for review.

PDF average compression ratio

OFD compression ratio

Supported national cryptographic algorithm suite

Layout fidelity

Provides standardized container images with Kubernetes orchestration support, launching compression services in minutes, suitable for cloud-native government platforms and containerized Xinchuang environments.

Compression services deployed within government intranets, with data never leaving the domain, supporting centralized management and unified operations, meeting classified protection and data security compliance requirements.

Provides Xinchuang bare metal installation packages, deploying directly in Kylin/UOS + Phytium/Kunpeng environments without requiring a container runtime, suitable for strict Xinchuang compliance scenarios.

Apply for an API Key and make your first document compression request using curl/Python/Java/Node.js

Complete OpenAPI 3.0 specification, online debugger, and error code reference

Rust SDK dynamic library, C FFI headers, Python bindings, and multi-language example code