Every company a scout has read against the five things that compound in an AI application business — the customer relationship, the proprietary data, the evals, the workflow and a domain-specific model — each banded owns / partial / none / unknown with its own citation, and each dated.
Beside those five, each card carries AI adoption depth — banded deep / moderate / shallow / unknown, cited the same way. It is a paired axis, not a sixth band: the five ask whether the value would accrue to this company if the thesis works, and this asks whether it is working at all. It is never counted in the “owns of 5” line and the two are never added together — a company can own neither its data nor its model and still run one of the deepest deployments anywhere. Where no reading has been made, the card says so plainly instead of guessing at one.
Grouped by vertical, alphabetical by name within it. The counts are a pattern, not a ranking and not a score: owning five together differs in kind from being excellent at three, which no single number expresses. unknown means nobody found the evidence, not that the answer is no. Weightless — nothing on this page ever reaches a signal, and none of it is a forecast of returns.
13 of 198 companies covered. Scouting is manual and condition-led, so the other 185 carry no profile — an absence of research, never a verdict of “owns none”.
Health
Scouted against the condition: a company that simultaneously owns the customer relationship, the data, the evals, the workflow, and a domain-specific model
2 owns · 2 partial · 1 unknown of 5 — a pattern, not a score
AI adoption: deep
Abridge is live at more than 300 health systems and supports over 100 million clinical conversations a year, at partners collectively serving roughly 250 million patients, with note generation in 28-plus languages. Named enterprise deployments include Northwestern Medicine, Johns Hopkins Health System, Emory Healthcare, UCHealth and Sutter Health, where 78% of clinicians report improved work satisfaction — daily encounter workflow, not an occasional assist. At Reid Health, nursing use accompanied a vacancy rate falling from 18% to 8.6% and a 70% cut in incidental overtime. The June 2026 platform launch extends the same ambient capture beyond ambulatory documentation into nursing, inpatient and revenue-cycle workflows, surfaces it had only begun to address. source: abridge.com
Owns the enterprise relationship and the whole encounter-to-billing chain at 300+ health systems, but the corpus belongs to those customers and the model is an adaptation of a third-party base. Evals are undocumented — notable for a product whose output enters the medical record.
Contracts with the health system itself — Northwell Health, the largest not-for-profit system in the Northeast, across 28 hospitals and 1,000+ outpatient facilities — rather than selling seats to individual clinicians; 300+ health systems in total. source: abridge.com
Trains on "de-identified data" from roughly 100 million conversations a year, but no proprietary corpus or exclusive right to it is described — the conversations originate in customer health systems, so the volume is real while the ownership is not established. source: abridge.com
Looked for a published validation methodology, an accuracy benchmark or a model card and found none; the cited evidence of quality is a KLAS market-survey ranking, which measures customer sentiment rather than clinical accuracy. Absence of evidence, not evidence of absence. source: abridge.com
The chain from encounter to billable record runs inside the product — pre-visit preparation, in-visit documentation and decision support, post-visit note generation, billing-code assignment, nurse documentation and clinical-trial screening, across 55+ specialties and 28+ languages. source: abridge.com
Building a foundation model for clinical conversations on NVIDIA Nemotron, trained on NVIDIA Blackwell infrastructure — the Nemotron base layer is a third party's open model and Abridge's adaptation sits on top, so the weights are customised rather than owned end to end. source: abridge.com
Hippocratic AI's April 2026 Polaris 5.0 release says the model was built on more than 180 million real-world patient interactions and validated by over 7,500 U.S.-licensed clinicians. The AI is the product rather than a feature: voice agents run post-discharge, medication-adherence and screening outreach calls as standing workflow at Universal Health Services, WellSpan Health, Cincinnati Children's, University Hospitals and Sheba Medical Center, with customer case studies citing a 30% readmission reduction and an 89% connection rate. The agent catalogue exceeds 1,000 prebuilt agents. Headroom remains visible: University Hospitals describes initial pharmacy-related deployments ahead of wider rollout, and the life-sciences arm dates only to a January 2026 acquisition. source: hippocraticai.com
Owns the customer and performs the patient interaction itself at real volume, but three of five dimensions are undocumented in the cited source. Included at that grain deliberately: the rubric records what is evidenced, and a company can carry serious traction while most of the stack stays unestablished.
Contracts directly with 50+ large health systems, payors and pharma clients across six countries, including named providers such as Cleveland Clinic, Northwestern Medicine, Ochsner Health and Moffitt Cancer Center. source: hippocraticai.com
Looked for a proprietary corpus or an exclusive data right and found none described; 115 million clinical patient interactions are reported as a volume of work performed, which is not the same as a claimed data asset. Absence of evidence, not evidence of absence. source: hippocraticai.com
The announcement names a "Polaris Safety Constellation Architecture" it will keep investing in, but does not define it, describe its methodology, or establish it as an evaluation system Hippocratic owns — so the band cannot be raised on the strength of the name alone. source: hippocraticai.com
The patient interaction itself happens inside the product: 115 million clinical patient interactions completed in the 15 months since commercialisation, rather than the product handing a task back to staff to perform. source: hippocraticai.com
The announcement does not state whether the company trains its own models or builds on a third-party base. Looked for a model description in the cited source and found none. source: hippocraticai.com
2 owns · 2 partial · 1 unknown of 5 — a pattern, not a score
AI adoption: deep
OpenEvidence is used by more than 40% of physicians in the United States, and founder Daniel Nadler describes it as the most widely used AI platform among American doctors — a point-of-care tool for high-stakes clinical decisions, not an occasional assist. The company says it has gathered hundreds of millions of real-world clinical consultations from verified physicians, and passed $100 million in annualized revenue in 2025 on advertising rather than subscriptions, with 95% of new users hearing about it from another physician. Depth is concentrated in one clinician type in one country: roughly six in ten U.S. physicians sit outside that base, and Nadler points to small practices without IT departments or software budgets as where most American care happens. source: cnbc.com
Owns the clinician relationship outright and trains its own vertical model, but rents the corpus that model is trained on and does not own the surface where care is recorded. The interesting asymmetry in the set: enormous reach on top of a licensed foundation.
Reaches the clinician directly and gates access itself — verification by NPI scanning or hospital email — with 40%+ of U.S. physicians using it daily and 65,000+ new verified registrations a month. No health-system contract stands between the product and its user. source: sacra.com
The corpus is LICENSED, not owned: NEJM and JAMA journals, Wiley biomedical literature, Cochrane systematic reviews and specialty-society content, with no exclusivity terms specified. A renewal it does not control sits under the whole product. source: sacra.com
Looked for a published benchmark or evaluation methodology and found none; deterministic citation linking is a retrieval mechanism that makes answers traceable, not a standard for judging whether they are clinically right. source: sacra.com
Consulted during care rather than being where care is recorded — the report names Epic and Oracle-Cerner as the EHR incumbents it sits against, so the clinical system of record remains someone else's surface. source: sacra.com
A vertical LLM trained exclusively on licensed medical texts, deliberately avoiding public-internet contamination, rather than a wrapper over a general frontier model. source: sacra.com
Tempus runs its AI in routine clinical practice, not pilots. Its FY2025 10-K reports connections to more than 55% of all oncologists practicing in the United States across over 700 data connections, about 367,500 clinical oncology samples sequenced in 2025 against roughly 31,000 in 2018, and revenue of $1,271.8 million. The Tempus Next care-gap engine covers 15 disease areas, is live in around 150 hospitals and screens more than 60,000 patients a month; its tumor-origin algorithm has been in clinical use since 2021; and the models draw on 8.6 million de-identified patient records and over 450 petabytes of data. Tempus notes that algorithmic diagnostics still represent only a small proportion of the diagnostics market, leaving Algos the least penetrated part of the platform. source: sec.gov
The rare case where the data rights are a contractual consequence of the customer relationship rather than a separate purchase: covering 55% of U.S. oncologists is what produces the longitudinal corpus its own foundation model is trained on. Evals are internal validation, not an owned standard.
The FY2025 10-K states its provider network "covers more than 55% of U.S. oncologists" — a direct contracting relationship with the ordering physician, not access resold through a distributor. source: sec.gov
The same 10-K sentence states the network "provides us with broad data rights, including the rights to longitudinally updated data" — the rights travel with the clinical relationship rather than being licensed in, and the library is described as 500+ petabytes across 45M+ de-identified patient journeys. source: sec.gov
Reports its own validation — a C-index of 0.802 for overall survival (p<0.001) in EGFR-mutant NSCLC — but this is internal measurement of its own model, not a published benchmark the field is scored against. Looked for a domain evaluation standard it owns and defines; found performance results instead. source: biospace.com
The 10-K describes running next-generation sequencing on behalf of the patient and providing clinical trial matching and data structuring services — the ordering, sequencing, reporting and matching steps run through Tempus rather than being handed back at the assay. source: sec.gov
Constructed and trained its own multimodal transformer foundation model on 2.5M longitudinal records — 250M+ pages of clinical notes, 450,000 digitized images and 500,000 genomic and transcriptomic sequences — rather than adapting a third-party base. source: biospace.com
