We design, build, and deploy custom RAG for healthcare trained on your data and wired into your stack. First scoping call this week - POCs in 27 days.
See why healthcare companies have trusted us since 2022.
We build healthcare winners
How we work with you on building custom RAG systems for healthcare
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Types of Healthcare RAG Systems We build
Where our customers focus
Width.ai uses a cutting edge understanding of the chatbot industry and the SOTA frameworks to develop RAG systems outperform off the shelf solutions.
Product medical document summarization system that ingests varied patient record layouts and reliably extracts essential medical data: names of medical personnel, diagnoses, ICD codes, medications, and dates of visits and procedures. All extracted data rolls into a single patient timeline summary, plus document-level summaries per record. 96% accuracy.
Utilizes a layout understanding model to dynamically adjust prompt rules based on recognized document formats and page types. Provides field-focused guidance to help the LLM link complex entities. By tailoring rulesets to relevant pages and skipping unnecessary ones, the system minimizes hallucinations and improves data recognition efficiency.
Our SOTA program synthesis system for doc processing allows us to build a machine learning algorithm to learn key -> value attribution rules (entity linking) through a trained relationship on common patient doc layouts. Creates understanding of key -> value distance, entity mapping with boxes and tables, and character size relationships.
Our SOTA framework for chatting with entire cases and depositions, exhibits, and medical records using a custom multi-agent approach with citations and validations. Built specifically to cover areas where semantic driven approaches fail.
Q&A system built on top of med doc processing architecture. Uses custom LLM + program-synthesis layer to classify page type and visit context, so questions about referring physicians, home-health care providers, and dosage return answers attributed to the correct doc and visit — not just the first match on the page. 96% accurate. Raised 8 figure series A.
