Infineon.
A reference-grade agent surface that AI finds about four times in ten.
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 Infineon, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on Infineon'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 Infineon, free for your domain. Five agent classes, one real task, your score in 48 hours.
Every agent class reaches the cart. The checkout asks for an account.
We asked five kinds of AI agent to find and order the AUIRFP4110 automotive MOSFET (TO-247) on infineon.com. Spec fidelity held the whole way: a plain fetch and a script both extracted the part from the finder context through to where-to-buy, and the browser agent completed all three runs. The agent reached the distributor cart. The one stop is the final continuation: cart and checkout require a registered account.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
Scope. This is one part in one portfolio. Infineon spans automotive, power and sensor systems, green industrial power, and connected secure systems.
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 source a component, does it route to Infineon?
Infineon comes up about a third of the time. The rest of the time, an agent recommends an alternative first.
Discoverability · 18-datapoint auditOnce an agent is on Infineon's site, can it place the order?
Every kind of agent gets through, and reaches order-ready.
Every agent class reaches order-ready, the purchase itself (login-walled) is untested · breakdown aboveWhat this does not yet cover
The agent reaches order-ready: product selected, price visible, no login. The actual checkout, cart and payment, sits behind a login wall and was not part of the test. The agentic purchase itself is unproven.
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.
The talent lane.
A candidate's agent, sent to find an open software-engineering role at Infineon and prepare the application. Same brand, second surface: the career portal.
Readable posting, unresolved apply step.
Both non-browser agent classes read the posting in full; the measured role sits in Bengaluru, because no Germany-based software posting was findable through machine-readable paths at lock time; the in-browser check is still pending.
Furthest close-state reached: spec match · 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 anywhere. Only the GET-reachable posting-detail page and a raw GET on the /api/apply endpoint were inspected (the latter returned a generic 404, not a form). Location note: the measured posting is located in Bengaluru, India, not the DACH region — a dedicated re-lock search for a live DACH (Germany/Austria/Switzerland) software/IT engineering posting on jobs.infineon.com and www.infineon.com/careers on 2026-07-02 found only dead (404) or stale/expired (200 with no structured JobPosting data) candidate URLs; none could be confirmed live within the paced-request budget. Whether login is truly required to submit (vs. an alternate guest-apply path), what artefacts (CV/cover letter) are needed, and the real form length remain unconfirmed and are deferred to the Phase C browser pass.
Fairness note
Infineon runs one global career platform for the whole group, and the posting measured here is a software engineering role in Bengaluru, locked after a documented same-day search could not confirm a comparable German posting through the same automated route. Because the platform renders every posting identically, the usability findings describe the surface itself rather than any single location. No application was submitted and no account was created; the candidate-account requirement at the application step is recorded as a documented fact, and the score reflects how far an automated assistant verifiably gets, measured the same way for every company in this index.
What this means for Infineon.
On product discovery, Infineon is not failing on surface quality, every agent class held spec fidelity from the part finder to the distributor cart. It loses earlier: on broad part-selection prompts, an agent reaches Infineon only about four times in ten. The reach is the gap, not the surface.
Discovery packaging, not surface repair. Reference Pages that concentrate the part-selection authority already in the product tree, Answer Pages for the application questions an engineer asks before naming a vendor, Concept Pages that own the parametric vocabulary, so Infineon is the cited part before the shortlist forms.
The proof isn't a cleaner datasheet. It's agents naming Infineon parts on broad application prompts, not only when the part number is already known, 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 Infineon. The gap is being found, not the channel.
No marketplace structurally displaces Infineon for this part class. Authorized distributors (Mouser, Digi-Key, Arrow) are the brand's own where-to-buy channel, reached directly from the product page, not an intermediary that captures the demand.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Infineon AUIRFP4110 Automotive MOSFET
- Close state
- order-ready
- Bottleneck
- Spec-fidel from finder to where-to-buy across all classes; only the cart/checkout continuation is gated by a registered account. The earlier gap is discovery, not the surface.
Fairness note
Wave 2 (a fleet wave Industrials). Single measured task (AUIRFP4110 automotive MOSFET, order_ready close state, checkout login-gated). AI Visibility 39.15 (18/18 valid datapoints across 3 providers); AI Usability derived from the a fleet wave access profile. Confidence C, single task; Fairness Review pending the sector fairness grid.
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]
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.
Measurement timeline.
Each wave appends; nothing overwrites. Frozen Wave Rule.
- Entry · 01
22 May 2026
Wave · ProtocolWAVE-Q2-2026-W2
ars-methodology/v1.1
First v3 measurement. AI Visibility 39.15 (18/18 valid datapoints, 3 providers, DE). AI Usability derived from a fleet wave: HTTP/coding/browser all success with full spec fidelity, ACT reached the distributor cart, checkout continuation gated by a registered account. Confidence C, single task.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 22 May 2026
AI Visibility audit · Infineon Wave Q2 2026
Internal · Hyperize evidence
- · AI Visibility score (18/18 valid datapoints across 3 providers)
- · the close state reached (order_ready)
- [S2]Accessed · 22 May 2026
Hyperize fleet · a fleet wave Industrials (access profile)
Internal · Hyperize evidence
- · how each kind of agent fared (HTTP/coding/browser success, ACT cart reached + checkout login)
- · the close state reached (order_ready, checkout login-gated)
- · the tested product (AUIRFP4110, TO-247)
- Accessed · 22 May 2026
Public · hyperize.ai
- · fairness declaration
- · Third-Party Interception framing
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.
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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.
