Sayvant

Ambient generation is the whole product and the company exists only to do it. Note for completeness rather than as a deduction: the underlying generative capability runs on Microsoft Azure OpenAI Service, so the differentiated work is the emergency medicine specific reasoning, note structure and clinical content layered on top rather than a foundation model built in house.

Draft and review, with the platform generating charts that providers review before signing, and vendor materials describing preservation of clinician control as a design goal. The HITLAB evaluation assessed error prevention and system visibility among its criteria, which is more external scrutiny of the interaction design than most vendors here invite. Held at B because no acceptance rate, edit burden figure or confidence threshold is published, and because the oversight question is sharper here than usual given what the system is designed to construct.

Names its own infrastructure, which is rarer than it should be: Sayvant states it is built on Microsoft Azure OpenAI Service, letting a buyer reason about the underlying model family and its known behaviour rather than treating the system as a black box of proprietary AI. It also describes its design philosophy explicitly rather than in adjectives, stating that most ambient tools capture what was said while Sayvant generates what the note needs to reflect. Held at B because no accuracy figure, model card, hallucination rate or evaluation methodology was located.

The most ambitious study design in this category, and held at B for who ran it rather than how it was built. Published June 2026, it analysed over 250,000 encounters across 50 emergency departments against a historical baseline of more than 20 million encounters spanning three years, at a scale intended to control for the confounders that drive variation in emergency medicine, and it is the first in this category to take patient throughput and professional fee capture as primary outcomes rather than charting time. That is a genuinely better question than most of the literature asks. But the study was conducted and published by the vendor, released through a press distribution service rather than a journal, with no peer review located and with outcome measures chosen by the party selling the product. Independent work exists alongside it: a HITLAB heuristic evaluation white paper with named academic authors, and a KLAS Spotlight. Peer reviewed publication of the multicentre analysis would move this to A.

Not assessed. No statement on audio or transcript retention, de identification, or training use was located in this pass.

Not assessed. No product specific business associate agreement posture was located in this pass.

Not assessed. No named or dated attestation and no trust centre located in this pass.

Not a regulated medical device and no clearance claimed. The relevant regulatory context here is billing rather than device: the 2023 CMS revisions to medical decision making documentation requirements are the stated driver for the product, which places it closer to coding compliance exposure than to FDA oversight.

Graded on incentive structure, following the coding drift reasoning applied in this index to Charta Health, Candid Health and Solventum, and Sayvant states the position more plainly than any of them. Its clinical AI lead describes an emergency department note as a structured argument for medical necessity and says that where most ambient tools capture what was said, Sayvant generates what the note needs to reflect. Its own headline study takes professional fee capture as a primary outcome, and its chief executive summarises the result as charges up and wait times down. A documentation system explicitly optimised to construct the justification for billing level, and marketed on the resulting revenue lift, carries an upcoding gradient that a compliance function must audit independently, because the tool and the clinician now share an interest in a more thoroughly justified chart. Credit where it is due: saying this out loud is more candid than competitors who market the same capability as completeness. No fairness, subgroup or accent disclosure was located despite support for more than 30 languages.

Not assessed. No named EHR integrations, integration architecture or write back mechanism was documented in this pass, which is a notable gap for a product deployed across 70 live sites and one of the first things to establish.

Partial. The hosting substrate is disclosed, running on Microsoft Azure OpenAI Service, which is more than most vendors in this category reveal and lets a buyer inherit Azure's regional and compliance story as a starting point. Nothing further was located on data residency configuration, regional deployment or sub processors.

No published rate card. Sold to emergency department groups and hospital systems. Note that the business case Sayvant itself advances is revenue rather than cost: its published outcomes are charge capture and throughput, so the return argument rests on professional fee capture increasing rather than on documentation labour falling, which is a materially different justification to take to a compliance committee.

Deliberately narrow, and the narrowness is the product rather than a limitation. Coverage runs across the acute care spectrum from freestanding emergency departments to Level 1 trauma centres, with urgent care alongside, and support for more than 30 languages. Emergency medicine imposes constraints most scribes are not built for, including encounter pace, disposition decisions and a documentation standard tied to medical decision making complexity. For an emergency department buyer this is the closest fit in the index; for anyone documenting scheduled outpatient visits it is not a candidate. Graded B on breadth, which is the axis, rather than on fit.