Invoices, contracts, audit packs, lab reports, and operational PDFs still arrive as unstructured files. We design intelligent document processing pipelines—OCR, layout-aware extraction, classification, and human review—plus LLM steps where summarization or Q&A adds value without replacing governance.
High-volume document discovery (search and cluster archives), metadata and requirement extraction for compliance reviews, financial and legal document structuring for downstream systems, and operations paperwork automation with clear escalation when confidence is low.
We combine OCR and layout parsing with rules, classical ML, and LLMs where each fits—extract tables and fields, route document types, and keep auditable logs. For research or regulated environments we align with your retention and privacy rules before any model sees raw content.
When contracts, health, finance, or internal policy data cannot go to public APIs, we architect pipelines that stay on your infrastructure: self-hosted OCR and layout services, open-weight models on your GPUs or CPU clusters, and queue-based processing so sensitive files never leave the network. Hybrid setups are common—local extraction and classification with optional cloud steps only for anonymized text, or fully air-gapped stacks where required.
We help you size hardware, choose models, and operate inference with monitoring and updates you control—not a black-box SaaS upload. Deeper model choices (fine-tuning, smaller specialized models) tie into our custom model work when off-the-shelf weights are not enough.
Start with a bounded corpus and measurable accuracy on real samples—not demo PDFs. Then harden ingestion, monitoring, and reprocessing when templates change. See related work on recent projects in energy document discovery, finance, and auditing support.
AI proof of concept · Data engineering · AI for SMEs · Custom & fine-tuned models