Document infrastructure for sovereign AI models.
Ingest complex Word contracts, multi-sheet financial models, scanned PDFs, and procurement XML into context-dense Markdown and JSON AST chunks. Completely air-gapped on standard CPU cores.
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
0 ms
0
-
0
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
90% of RAG pipelines fail because of flawed document parsing, not weak LLMs.
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 column-to-row integrity for 100% vector recall.
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 inside your private VPC.
Zero document bytes leave your firewall.
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.
Strict Air-Gapped Operation
Full compliance with GDPR, HIPAA, and SOC2. Zero external telemetry, zero vendor lock-in, and absolutely zero training on your data.
Zero-GPU Overhead
Runs efficiently on standard commodity CPU cores (x86/ARM). Preserves 100% of costly GPU VRAM solely for your inference models.
Zero-Retention Auto-Purge
Uploaded documents are converted in an isolated ephemeral runtime and hard-deleted from disk the millisecond parsing finishes. Zero document buffers or binaries are persisted to the database.
At 250,000 corporate pages/month, variable cloud ingestion fees accumulate to $30,000–$75,000 annually with recurring compliance and data residency risk.
Unlimited documents, unlimited pages, deterministic throughput. Eliminates variable billing surprises and achieves full payback within 60 to 90 days.
Engineered for zero-GPU commodity CPU execution.
Hardware Pod Sandboxing
Each incoming document receives an isolated ephemeral environment. Concurrent conversions never collide or corrupt memory.
Deterministic Table Protection
Multi-row table structures are parsed into native JSON objects and embedding-optimized descriptive strings for maximum RAG retrieval accuracy.
100% Air-Gapped Operation
Runs completely local on your Hetzner instance or enterprise pods. Zero external telemetry or vendor lock-in.