Document infrastructure for sovereign AI models.
Extract structured content from Word documents, spreadsheets, XML, HTML, and presentations into Markdown and JSON suitable for RAG pipelines. Local processing depends on your deployment and enabled extraction providers.
Interactive Ingestion Studio
Test the extraction pipeline directly in your browser. Documents are processed locally on isolated CPU cores and structured for immediate vector ingestion.
Upload document or take a photo with camera
Drag & drop contracts, spreadsheets, or take a picture of a hard copy document from your phone.
Executing CPU sandboxed extraction...
Building LIFO hierarchy stack without context drift
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Simple, high-throughput REST API.
Programmatically submit single or batch documents to your sovereign endpoints. Select between semantic vector chunks, clean markdown, or complete JSON AST trees.
/api/v1/extract
Direct extraction endpoint for immediate document parsing under 35 seconds.
/api/v1/extract-async
Asynchronous worker endpoint returning an instant jobId for 100+ page heavy tenders or multi-sheet models.
/api/v1/jobs/:jobId
Poll extraction status and retrieve completed JSON AST payload without HTTP timeouts.
# Extract document into Semantic Chunks (RAG) curl -X POST https://aidocengine.com/api/v1/extract \ -H "x-api-key: ade_live_your_api_key_here" \ -F "format=chunks" \ -F "document=@/path/to/document.pdf" # Supported formats: 'chunks' (RAG), 'markdown' (Clean MD), 'json' (Universal AST) # Supported files: .pdf, .docx, .xlsx, .pptx, .xml, .jpg, .png, .webp
Document extraction quality can affect retrieval quality in RAG pipelines. Validate parsing against your own documents and evaluation set.
Leading frontier models cannot answer questions accurately if standard OCR tools flatten your tables, scramble multi-column layouts, and strip away document hierarchy. AiDocEngine solves the raw data ingestion bottleneck.
Tabular Matrix Destruction
Conventional extractors read multi-column financial sheets and procurement specifications line-by-line, merging rows and destroying column relationships into meaningless word soup.
Converts multi-row grids into dual outputs: a native JSON AST object and a search-optimized natural language embedding string per row, preserving table row and column context for downstream retrieval.
Real-World Camera Photos & Handwritten Annotations
Field teams and operations staff frequently photograph signed agreements, delivery manifests, and invoices on mobile devices under suboptimal angles, shadows, and with handwritten margin notes.
Integrated document vision models isolate handwritten notes into explicit blockquotes (> [Handwritten Note]) and capture interactive checkbox states ([x] / [ ]) without hallucination.
Context Drift in 100+ Page Legal Agreements
Standard token splitters chop arbitrary paragraphs at fixed boundaries (e.g. 500 tokens). A liability subclause on page 84 loses all association with its parent section and governing terms.
Every generated chunk carries a persistent LIFO hierarchy breadcrumb path (e.g., ["Master Agreement", "Section 14", "Article 2.1"]) directly inside the chunk metadata for bulletproof semantic search.
Fragmented Ingestion Stacks
Engineering teams spend months stitching together separate open-source libraries for DOCX, spreadsheets, XML feeds, and PDF parsing—resulting in inconsistent JSON outputs and brittle maintenance.
A single unified REST endpoint handles Word, Excel, PowerPoint, XML, PDF, and high-res mobile photos, standardizing all output into a deterministic Universal Document JSON (UDJ) schema.
Deploy in your own environment.
Keep processing under your deployment controls.
Designed for financial institutions, defense contractors, healthcare organizations, and legal departments with strict data residency mandates. Ingest highly confidential contracts, financial models, and records without external third-party API exposure.
Self-Hosted Configuration
Self-hosted deployments can keep document processing within your environment when cloud-based extraction providers are disabled. Compliance depends on your configuration, policies, and operational controls.
Zero-GPU Overhead
Runs efficiently on standard commodity CPU cores (x86/ARM). Uses CPU-based extraction paths for supported office and text formats; actual resource usage depends on document size and deployment.
Zero-Retention Auto-Purge
Uploaded files are written to temporary storage and removed after processing completes. Configure filesystem permissions, worker isolation, backups, and retention controls for your deployment.
Cloud processing costs vary by provider, document type, OCR usage, and volume. Compare current vendor pricing against a measured workload and include storage, operations, and compliance costs.
Capacity and total cost depend on hardware, document mix, concurrency, and operational requirements. Run a representative benchmark before estimating savings or payback.
Engineered for zero-GPU commodity CPU execution.
Isolated Conversion Profiles
Office conversions use separate temporary LibreOffice profiles to reduce profile conflicts. This is not an OS-level security sandbox; run the service with restricted permissions and resource limits.
Deterministic Table Protection
Where table structure can be identified, rows are represented as structured data and searchable text. Accuracy should be measured against labeled source documents.
Self-Hosted Processing Option
Supported office, text, XML, HTML, and PPTX extraction paths can run locally. Cloud OCR is optional and sends document content to the configured provider when enabled.