The medical-legal workflow
is broken. We’re fixing it.

Medical-legal cases generate thousands of pages of records. Today, getting those records reviewed means cold-calling physicians, shipping boxes of unstructured documents, negotiating retainers over email, chasing W-9s, and waiting weeks for a callback — all before anyone knows whether the case has merit.

On the expert side, physicians receive massive document dumps with no chronology, no context, and no structure. They spend hours sorting records before they can apply any clinical judgment. Engagement letters are cobbled together from templates shared in Facebook groups. Payment is uncertain. The whole process is manual, fragmented, and expensive for everyone involved.

The emerging AI tools don’t solve it either. Dragging medical records into a general-purpose chatbot doesn’t work. These documents are too large, too complex, and too sensitive. A useful analysis requires clinical training — understanding what constitutes a standard-of-care deviation, how causation chains work in medical malpractice, what opposing counsel will argue, and which findings are clinically defensible versus speculative.

That’s not something an untrained model can do.

Annalis is purpose-built for medical-legal case evaluation. Our AI handles documents of any size, automatically redacts PHI, and delivers a structured analysis — medical chronology, standard-of-care flags with severity ratings and page references, causation mapping, damages indicators, and deposition questions — in minutes.

The analysis engine was developed with clinical methodology from a practicing surgeon who has reviewed cases as an expert witness. It doesn’t just summarize — it identifies what’s clinically defensible, anticipates opposing counsel’s arguments, and flags the findings that matter.