Original research · Pharma & Healthcare

Fresenius.

Last measured · 24 May 2026 Wave · Q2-2026-W5-LIFE-SCIENCES Tier · proprietary Confidence · C
Brand
Fresenius SE & Co. KGaA
Agent success

A working booking portal that only browser agents can drive end to end.

Bottleneck Discovery
Rarely found for the category.
4.6 /10
Agent Success Score
AI Visibility 41 / 100

Found & recommended by AI agents

AI Usability 44 / 100

Can an agent use the surface to get the job done

Coverage · 2 of 6 lanes measured Commerce lane · Wave Q2-2026-W5-LIFE-SCIENCES
Commerce 4.6
Talent usability measured
After-sales
Procurement
Investor
Press

This page measures two lanes. Commerce: can an agent find Fresenius, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on Fresenius's own career surface and prepare the application. After-sales, procurement, investor and press lanes run on different surfaces and are not yet measured.

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The test

The €0 booking step a browser reaches and a plain reader can't.

We asked five kinds of AI agent to find a Helios clinic in Berlin offering orthopaedic surgery for an ACL reconstruction, then reach an appointment-booking path. The plain reader fetched helios-gesundheit.de and got a partial render, clinic names loaded, department detail did not. The coding agent did marginally better with structured selectors but still missed specialty filtering. The browser navigated to the Berlin-Buch clinic page, clicked Termin buchen, and reached patienten.helios-gesundheit.de/appointments/book-appointment?facility=232 at step 2 of 6, with Orthopädie selectable. ACT completed the same flow.

One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:

Plain reader reads your raw page text, no browser Partial
Search assistant finds you through search Not yet run
Coding agent a script hitting your site Partial
Computer-use agent clicks and types like a person Succeeded
Autonomous operator runs the whole task unattended Succeeded

Scope. This is one specialty at one Helios clinic. Fresenius through Helios operates 89 hospitals across Germany with 100+ medical specialties, each with its own department structure and booking path.

Commerce lane

Found, and able to transact?

Two questions, measured separately. A brand can be recommended and still un-buyable, or perfectly buyable and never found.

AI Visibility

When someone asks an agent to find and order a kit, does it route to Fresenius?

41 / 100

Fresenius comes up about four times in ten. The rest of the time, an agent recommends an alternative first.

Discoverability · 18-datapoint audit
AI Usability

Once an agent is on Fresenius's site, can it place the order?

44 / 100

Computer-use agent and Autonomous operator reach the booking form; Plain reader and Coding agent get there only in some runs.

Browser-class agents reach the booking form; text / code-class agents reach it only partially, submission was not completed · breakdown above

Evidence · 70 / 100 A measure of how provable and consistent the result is, grounded in cross-method ground-truth agreement (the methods that ran returned the same price), not a separate measured run. A confidence layer on the two scores above, not a third sales axis.

Talent lane

The talent lane.

A candidate's agent, sent to find an open software-engineering role at Fresenius and prepare the application. Same brand, second surface: the career portal.

AI Usability · Talent lane

Application-ready for agents.

A prior measurement of Fresenius SE had drifted onto Fresenius Medical Care's own career site, a separate, independently DAX-listed brand; re-sourced directly on Fresenius SE's own career surface, an agent can read a full IT-security job posting end to end and reach the actual apply step, where the site itself states that submitting an application requires creating an account on an external system.

Furthest close-state reached: application ready · frozen task locked 2026-07-02

68 AI Usability / 100 · derived, not hand-rated
Plain reader fetches the posting page raw, no browser pass re-measured 2026-07-02 ~02:55 vs new SE lock (karriere.fresenius.de Consultant IT Security): 200, 69.9KB, title + Responsibilities in raw HTML
Coding agent parses the posting into structured fields pass same fetch: spec fields present as parseable page text (no JobPosting JSON-LD — extraction via HTML parse)
Computer-use agent clicks through the career portal like a person pass Phase C: full JD renders; Apply-now interstitial states account creation required — threshold not crossed

AI Visibility · in measurement — the audit run for this lane has not landed yet. No Talent-lane composite is shown until both axes are measured.

What this does not yet cover

This measurement did not test or attempt the actual application submission — no account was created, no form was filled, and no application was ever sent. The concrete form length, CV/cover-letter requirements, and the external application system's own access characteristics remain unverified and are out of scope for this browser-entity-correction pass. A standard HTTP/coding-class pass against the new lock URL has also not yet been run.

Fairness note

Fresenius SE and Fresenius Medical Care are separate, independently listed companies; this measurement was sourced on Fresenius SE's own career site after an earlier pass that had drifted onto FMC's site was discarded. The score reflects a single, dated IT job posting: an AI agent read the full posting and reached the application entry point, where the site requires creating an applicant account. That account requirement is recorded as a fact, not a penalty, and no application was submitted and no personal data was used at any point.

What's next

What this means for Fresenius.

Diagnosis

On healthcare-facility discovery, Helios is not lacking a booking portal, patienten.helios-gesundheit.de carries the appointment flow with the right step depth and facility pre-selection. It is losing the agent classes that can't render JavaScript. The clinic finder lands the brand correctly on text-only fetches; the specialty filter and the department detail don't render until the browser does. A patient using a text-only assistant can find "Helios Berlin-Buch exists" but not "Helios Berlin-Buch does orthopaedic surgery."

What changes the outcome

The fix is not removing the JS render, the patient portal trades off interactivity for it. It is publishing the SSR shadow of the clinic finder: Reference Pages per clinic carrying clinic + specialty + booking URL in plain HTML, Answer Pages for "Welche Helios-Klinik macht X in Y?", a structured-data layer over the booking portal so the appointment endpoint is reachable from a text-only fetcher with the right facility ID embedded.

What proof looks like

The proof is not a redesigned portal. It is plain readers and coding agents extracting "Helios Berlin-Buch, Orthopädie, Termin-URL /book-appointment?facility=232" directly, alongside the existing browser path, re-measured each wave.

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Channel position

No intermediary stands between agents and Fresenius. The gap is being found, not the channel.

Channel position derivation on the Wave 5 the AI Visibility pass response texts is queued; the displayed share values are the unmeasured default. The measured task closes at Helios's own booking portal (patienten.helios-gesundheit.de), so the close-state is brand-owned rather than intermediated; the discovery competition with hospital directories will be characterised in the next pass.

0% direct
0% via intermediary
Frozen task slate

Hyperize-selected tasks.

One task from the public sector grid. Task list is frozen before each wave runs.

Helios Berlin-Buch — orthopaedic surgery, ACL booking

Close state
the booking form
Bottleneck
Department detail hidden behind JS render; text and code agents get clinic identity but not specialty match; browser completes booking portal step 2 of 6.

Fairness note

Wave 5 Q2 2026 audit complete on a single task (Helios Berlin-Buch Orthopädie ACL booking, booking-form-ready close state). AI Visibility from the audit pass on an unbranded informational probe (Privatklinik-Kreuzband-OP-Berlin). AI Usability from a fleet wave (2026-04-07) full phase 1-4. Fairness Review pending the sector fairness grid.

Methodology

How the score was produced.

Discoverability is audit-pipeline-derived. 3-provider sample (openai, perplexity, anthropic), 3 query variants per task, 2 runs per variant · 18 valid datapoints scored against a five-state handoff cascade. [S1]

AI Usability is derived from the access-profile above (usability-derivation/v1): how far the best agent reached (close state) modulated by how many agent classes succeeded. The per-class profile is the truth; the score is a reproducible summary of it, not a separate rating. Fleet phases (HTTP / Coding / Browser / ACT) produce the profile. [S2]

Formula

Agent Success Score = (AI Visibility × 0.20) + (AI Usability × 0.70) + (Evidence × 0.10)

On a 0–100 scale, displayed 0–10. AI Usability bundles the agent's reach + completion; AI Visibility is audit-derived discoverability. Weighting is public; the per-prompt derivation is not.

Measurement scope

Confidence C · one measured task on a 3-provider track (openai, perplexity, anthropic). Confidence promotes to B with a second task plus a fourth provider on the next wave.

History

Measurement timeline.

Each wave appends; nothing overwrites. Frozen Wave Rule.

  1. Entry · 01

    07 Apr 2026

    Wave · Protocol

    a fleet wave

    fleet/measured

    First-pass FLEET measurement: Helios Berlin-Buch ACL booking across ceiling + http + coding + browser-agent + act breeds. Found JS-rendered specialty detail blocking text/code agents from full specialty match; browser success 3/3 Score 86.7; ACT booking-form-ready at step 2 of 6 with facility pre-selected.

  2. Entry · 02

    24 May 2026

    Wave · Protocol

    WAVE-Q2-2026-W5-LIFE-SCIENCES

    ars-methodology/v1.1

    Wave 5 kickoff. AI Visibility audit running on unbranded informational probe (Privatklinik-Kreuzband-OP-Berlin). AI Usability carried from a fleet wave full phase 1-4 access profile.

Sources

Evidence and provenance.

Public methodology references and internal evidence pointers behind every claim above.

  1. [S1]

    AI Visibility audit · Fresenius Wave Q2 2026 (unbranded informational)

    Accessed · 24 May 2026

    Internal · Hyperize evidence

    • · AI Visibility score (audit-derived, 18 datapoints, 3 providers)
    • · AI platforms queried (openai/perplexity/anthropic)
    • · close state reached (booking_form_ready)
  2. [S2]

    Agent-fleet run · Life Sciences · Helios phases 1-4

    Accessed · 07 Apr 2026

    Internal · Hyperize evidence

    • · per-class access profile (text/code partial; browser success 3/3 with Score 86.7; ACT booking step 2/6)
    • · JS-render finding (clinic identity at HTTP, specialty detail only after JS)
    • · booking-form-ready ACT (patienten.helios-gesundheit.de book-appointment URL with facility=232)
  3. [S3]

    Ground-truth ceiling run · Helios Berlin-Buch Orthopädie booking

    Accessed · 07 Apr 2026

    Internal · Hyperize evidence

    • · Ground-truth clinic (Helios Berlin-Buch)
    • · Department verification (Orthopädie selectable at booking step 2/6)
  4. Accessed · 24 May 2026

    Public · hyperize.ai

    • · fairness declaration
    • · Third-Party Interception framing
Last updated · 24 May 2026 Next review · 30 Sept 2026 Wave · Q2-2026-W5-LIFE-SCIENCES Tier · proprietary Confidence · C Index score · 4.6/10 Machine-readable record
Universe: DAX 40 composition as of 2026-07-01, reviewed after each Deutsche Boerse index review.

Editorial coverage

The DAX 40 Agent Success Index is a point-in-time snapshot of the agent-success of public digital touchpoints. Results are not statements about product quality, company performance, service quality, or the legal obligations of the brands named. Brand names and logos remain the property of their respective owners and are used solely for identification and reporting purposes in the context of editorial coverage (§ 23 MarkenG, Art. 5 GG).

Brands wishing to respond, engage, or correct a factual error may contact hello@hyperize.ai. Responses received are published in full alongside the findings. Full methodology and editorial-coverage notice: coverage statement.