Qiagen.
A near-flawless surface that mass-market AI rarely finds.
Found & recommended by AI agents
Can an agent use the surface to get the job done
This page measures two lanes. Commerce: can an agent find Qiagen, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on Qiagen's own career surface and prepare the application. After-sales, procurement, investor and press lanes run on different surfaces and are not yet measured.
Your brand isn't measured yet
What does an AI agent do with your website?
The same measurement as Qiagen, free for your domain. Five agent classes, one real task, your score in 48 hours.
The €5,480 diagnostic kit you can order with a curl command.
We asked five kinds of AI agent to find and order a CE-IVD diagnostic kit on qiagen.com. Every type that ran succeeded, even the simplest: the page is server-rendered, so a plain fetch reads exactly what a browser sees. The agents found the therascreen EGFR Plus RGQ Kit (catalog 874611, €5,480), confirmed the 42 mutations it detects, and added it to the cart with no login. The surface is near-flawless. The catch sits one step earlier: broad category questions almost never surface Qiagen at all.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
Scope. This is one diagnostic kit. Qiagen offers 180+ IVD-classified products across oncology, infectious disease, and companion diagnostics, each with regulatory classification and sample-compatibility filtering.
Found, and able to transact?
Two questions, measured separately. A brand can be recommended and still un-buyable, or perfectly buyable and never found.
When someone asks an agent to find and order a kit, does it route to Qiagen?
Qiagen comes up about one time in ten. The rest of the time, an agent recommends an alternative first.
Discoverability · 24-datapoint auditOnce an agent is on Qiagen's site, can it place the order?
Every kind of agent gets through, and reaches a working cart.
Every agent class reaches a working cart, final payment is a separate step · breakdown aboveHow far the agent actually got
Close state is cart-ready: the agent reached a working "Add to cart" with no login gate. The transaction path is real, not just order-ready. (Final payment is a separate step.)
Evidence · 98 / 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.
The talent lane.
A candidate's agent, sent to find an open software-engineering role at Qiagen and prepare the application. Same brand, second surface: the career portal.
Application-ready for agents.
Both non-browser agent classes read the posting via a single plain GET, with title, requirements, and location fully present in server-rendered JobPosting JSON-LD; the application entry sits behind a Workday candidate-account login/create-account gate that only renders client-side.
Furthest close-state reached: application ready · frozen task locked 2026-07-02
The score above is provisional where a class shows untested: untested classes are excluded from the calculation, so the pending check can only confirm or raise the value, never silently lower it.
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 submit an application, upload any file, create an account, or log in. Only the GET-reachable posting page and the /apply route's static (pre-JS) shell were inspected; the login gate itself was inferred by pattern-matching against the sibling Qiagen posting (JR00000518) already probed in the scout, not independently confirmed for this specific req ID. Whether the full multi-step apply flow completes successfully end-to-end is untested, and no application was ever submitted.
Fairness note
Qiagen's careers site hands off to a dedicated, Qiagen-branded Workday instance; we count this as Qiagen's own career surface, the same rule we apply to every company using a branded applicant system. The posting itself is fully machine-readable via a plain page request for the agent types we tested; the candidate login required before applying is standard practice and is documented as a fact, not scored as a deficit. No application was submitted, no account was created, and browser-based agents were not part of this measurement.
What this means for Qiagen.
On product discovery, Qiagen is not losing on surface quality, the order-ready path holds without login, server-side rendering makes every page machine-readable. It loses on category entry: 83 percent of category-prompt traffic never reaches qiagen.com. Expert intent finds the brand; broad intent doesn't.
The fix is category-entry packaging, not surface repair. Concept Pages that own the diagnostics vocabulary, Answer Pages for the broad questions a researcher asks before they know the vendor, Reference Pages that concentrate the authority already buried in deep product pages.
The proof isn't more pages. It's agents surfacing Qiagen on broad category prompts, not only expert intent, re-measured each wave.
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The same measurement runs free against your domain: five agent classes, one real buying task, your Agent Success Score in 48 hours, in the exact format of this page.
No intermediary stands between agents and Qiagen. The gap is being found, not the channel.
Qiagen is not structurally displaced by intermediaries. Expert-intent queries route to qiagen.com directly. The 83 percent of category-prompt traffic that doesn't reach the brand isn't captured elsewhere, it simply does not surface the brand at all. A discovery problem, not a channel problem.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
therascreen EGFR Plus RGQ Kit
- Close state
- a working cart
- Bottleneck
- Broad-category prompts do not surface qiagen.com; expert-intent does.
Fairness note
Single measured task (therascreen EGFR Plus RGQ Kit), scored under the public Task Selection Doctrine. Confidence B: full datapoint coverage on one task, promotion to A requires three or more tasks. Third-Party Interception none, no intermediary obscuring measurement. Measured in English, the primary buyer language for expert diagnostics globally; the rest of the index is measured in German, and a German re-measurement is scheduled for the next Qiagen review to keep cross-brand comparability.
How the score was produced.
Discoverability is audit-pipeline-derived. 4-provider sample (openai, perplexity, anthropic, google), 3 query variants per task, 2 runs per variant · 24 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]
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 timeline.
Each wave appends; nothing overwrites. Frozen Wave Rule.
- Entry · 01
17 May 2026
Wave · ProtocolWAVE-Q2-2026-PILOT
ars-methodology/v1.1
First v3 audit-derived AI Visibility (11.51, 24/24 valid datapoints). AI Usability from agent-fleet phases: HTTP/Coding/Browser 3/3, ACT add-to-cart with no login. Confidence B, single measured task.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 18 May 2026
AI Visibility audit · Qiagen Wave Q2 2026
Internal · Hyperize evidence
- · AI Visibility score (audit-derived)
- · sample size + provider coverage
- · channel position broad-category narrative
- [S2]Accessed · 16 May 2026
Hyperize fleet · agent-fleet phases (HTTP/Coding/Browser 3/3 + ACT add-to-cart; source for C/A/E)
Internal · Hyperize evidence
- · AI Usability and Evidence inputs
- · fleet test outcomes
- Accessed · 18 May 2026
Public · hyperize.ai
- · fairness declaration
- · task class assignment
Read the doctrine. Challenge the score. Extend the slate.
Task Selection.
The fairness doctrine behind the slate above. Five failure modes, six criteria, public before each wave.
Read ChallengeDisagree with this score.
Send evidence under public Fairness Review. Failed reviews are documented with the named failure mode.
Challenge ExtendSubmit your own task.
Open Surface Run · additive measurement. The Hyperize-selected slate stays frozen; your task gets the same methodology.
SubmitEditorial 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.
