About
The medical evidence engine.
AskStein turns medical literature into answers clinicians can inspect and verify. Every answer is grounded in retrieved evidence, linked to its source, and designed to abstain when the evidence is not strong enough.
Medical knowledge is enormous, fragmented, and constantly changing. PubMed alone contains more than 40 million citations, with roughly one million more added each year. Clinicians need a faster way to find the relevant evidence without giving up the ability to inspect the source. General-purpose language models can sound convincing while producing unsupported claims or invented references. In medicine, fluent is not the same as trustworthy.
At its core, AskStein is not a model — it's a retrieval system wrapped around one. That distinction is the whole point. The trustworthiness lives in three places:
Corpus
A curated, de-duplicated body of biomedical literature drawn from legitimate open-access sources.
Retrieval
The machinery that finds the most relevant passages for a given question.
Citations
A deterministic layer that guarantees every reference in an answer is real and correctly attributed.
The language model's job is deliberately narrow: read the retrieved evidence and write an answer grounded in it. It is explicitly not permitted to invent citations, and when retrieval doesn't surface adequate evidence, the system abstains. The intelligence people trust isn't the model's fluency — it's the curation and the discipline around it.
The orthopedic engine is the first proof point. In the team's internal 200-question evaluation, AskStein reported 97.5% grounding, 99.5% faithfulness, 96.0% relevance, and 94.5% abstention performance, with no fabricated citations observed. These are internal development results, not a substitute for independent clinical validation. Read the evaluation notes →
AskStein's central bet — confirmed empirically in its own testing — is that retrieval quality, not model size or fine-tuning, is the dominant driver of answer accuracy. Improving the corpus and the retrieval pipeline moves the needle more than swapping in a bigger model. That's why the curated, citation-grade corpus is the competitive advantage, not the model. Anyone can call an LLM; the defensible asset is the curated body of evidence and the standards that keep it clean.
AskStein is built around four product principles:
Curation
The corpus is deliberately assembled and auditable, not an opaque black box.
Depth
Rather than covering all of medicine shallowly, AskStein goes deep in one specialty first.
Integrity
Citations are guaranteed by architecture, not coaxed out of a prompt.
Honesty
The system is built to say "I don't know" rather than to always produce an answer.
The roadmap starts with orthopedic evidence retrieval and expands toward a multi-specialty evidence layer. Over time, AskStein is designed to support pre-visit intelligence, point-of-care decision support, treatment monitoring, post-care follow-up, and revenue integrity — with the clinician reviewing and deciding at every stage.
The long-term direction is a literature-grounded platform that can connect with clinical workflows and return a workup-complete evidence packet. Those workflow integrations remain roadmap items; today's product is the orthopedic evidence engine. Meet the team →
The literature is real. The citations are real. The opportunity is all of medicine.