Urjasoft - Software, AI & SaaS Engineering
Founded 2016 • 150+ Projects • 14 Countries
Prototype investigating layout-aware spatial extraction from technical documents.
An exploratory document processing pipeline evaluating whether preserving 2D coordinate bounding boxes during OCR improves key-value pair extraction from complex layouts.
Traditional OCR tools strip out spatial layout, table hierarchies, and engineering callouts, making complex technical PDFs impossible to search accurately.
Combining layout-aware computer vision models with hierarchical chunking preserves semantic relationships across multi-column pages and engineering drawings.
Multi-stage execution model separating probabilistic reasoning from deterministic state mutations.
PDF ingested at 300 DPI with automated skew correction and contrast calibration.
Vision models detect paragraph boundaries, table boxes, and callout coordinates.
Text tokens extracted retaining 2D coordinate positions relative to document headers.
Dense vector embeddings paired with text indices for sub-second retrieval.
“Observed Finding: Preserving 2D bounding-box spatial coordinates alongside OCR text tokens improved key-value extraction accuracy over treating documents as raw linear text streams.”
Findings from this research area inform our engineering capabilities in ai & automation solutions.
An experimental architecture evaluating whether combining AST parsing with LLM reasoning can synthesize deterministic regression suites without manual authoring.
An exploratory agent framework testing whether enforcing JSON Schema validation gates and cryptographic approval tokens between LLM decision steps prevents unintended API mutations.
An analytical research initiative evaluating whether lightweight statistical models can outperform simple moving averages on sparse, seasonal eCommerce catalog purchase data.
Our engineering team can evaluate your technical roadmap, suggest architecture patterns, and partner with you on high-assurance system execution.