Unstructured medical and research data is hard to process
Clinical notes, PDFs, lab reports, and publications come in varied formats, making reliable extraction and normalization difficult with generic AI pipelines.
Accelerate clinical and research workflows with AI-powered document analysis, data extraction, and knowledge retrieval – built for accuracy, scale, and domain-heavy datasets.
Manual review, siloed datasets, and weak AI pipelines delay insights across clinical studies, medical documents, and research archives.
Clinical notes, PDFs, lab reports, and publications come in varied formats, making reliable extraction and normalization difficult with generic AI pipelines.
Non-specialized models often misinterpret medical terminology, tables, and study structures – reducing accuracy and trust in downstream analysis.
Without traceable pipelines, versioned models, and reproducible outputs, researchers struggle to validate AI-assisted findings and meet compliance needs.
Extract structured data from clinical PDFs, reports, forms, and scanned records into large-scale knowledge bases.
Automatically summarize papers into sections, tables, citations, and figures for faster access.
Run models optimized for biomedical and scientific language across modalities.
Elevate retrieval performance across journals, trial data, and internal research knowledge bases.
Analyze thousands of documents and datasets in parallel on GPU-powered infrastructure.
Maintain logs, versioning, and reproducibility metrics for regulated and high-assurance environments.
Use high-accuracy OCR, long-context, & domain-capable models designed for complex documents & research-heavy workloads.
Planning large-scale research or clinical AI deployment? Work with our team to design a production-ready pipeline.
"Qubrid's medical OCR and research parsing cut our document extraction time in half. We now have traceable pipelines and reproducible outputs that meet our compliance requirements."
Clinical AI Team
Research & Clinical Intelligence