Continental.
Continental owns the premium-tire story.
Found & recommended by AI agents
Search-class agents touched the close in some runs; the full agent-fleet access profile lands in a later wave.
This page measures two lanes. Commerce: can an agent find Continental, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on Continental'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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Continental's website holds up. Tire retailers own the checkout.
We tested how AI agents handle a real purchase: a Continental premium summer tire in 225/45 R17. 18 measurements, three AI providers, German queries, zero errors. The full agent test has not run yet; a follow-up measurement is scheduled.
The Continental site holds up. It carries the PremiumContact 7 specification, the tire finder, and the size guidance, in a form agents can read. The purchase moves elsewhere. Reifen.de, Tirendo, Euromaster, and A.T.U hold the stock, the fitting appointment, and the checkout.
Continental makes the tire. The retailer makes the sale. One note: Continental is also a major automotive-technology supplier (ContiTech, ADAS electronics). This test covers the consumer-tire business, not the whole company.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
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 the right summer tire for a passenger car, does it route to Continental?
Continental comes up about a quarter of the time. The rest of the time, an agent recommends an alternative first.
Discoverability · 18-datapoint auditOnce an agent is on Continental's site, can it order the fitted set through a tire retailer?
Search-class agents surface config-ready in some runs; the full agent-fleet access profile lands in a later wave.
Evidence · 65 / 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 Continental and prepare the application. Same brand, second surface: the career portal.
Readable posting, unresolved apply step.
The posting itself reads cleanly for non-browser agents; a Germany-based software role could not be found through the career site's own machine-readable paths at measurement time, so a Bengaluru posting served as the frozen task.
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
Only the posting page was successfully fetched via GET; the external SmartRecruiters apply-entry page returned a bot-challenge (403) on both the initial request and one paced retry, so login requirement, CV/cover-letter requirements, and form length could not be read. No login was performed, no account was created, no form fields were filled, and no application was ever submitted. Location honesty note: the measured posting is in Bengaluru, India, not DACH -- a dedicated Scout-level DACH re-lock search (12 live IT-engineering postings checked) found zero DE/AT/CH candidates, so this non-DACH posting is measured as the documented fallback, not a silent substitution.
Fairness note
This measurement covers a single, real job posting on Continental's own career portal (DevOps Engineer, Bengaluru, REF89166B, captured 2 July 2026); no application was submitted, no form was filled, and no personal data was used. Continental runs one unified group-wide career portal whose posting pages are openly readable by automated agents, and the score reflects how far an agent gets in the regular reading and application-discovery flow, not the quality of Continental's hiring process. The bot challenge on the application entry page is recorded as a documented access condition; deliberate protection of application systems against automated traffic is a legitimate design choice, not a fault.
What this means for Continental.
On product discovery, Continental is not lacking findability. The brand is a premium tire maker named in every premium-summer-tire question alongside Michelin and Bridgestone. The gap is not the brand-own surface. It is the retail-intermediated purchase mode: the 225/45 R17 tire order does not close on continental-tires.com. It runs through tire retailers and comparison sites (Reifen.de, Tirendo, Euromaster, A.T.U). The agent reaches Continental, reads the PremiumContact spec and the fitment, then the order routes through a retailer. Continental owns the premium-tire story, the retailer owns the checkout.
What changes the outcome is closing the surface-to-retailer gap. The brand-own page surfaces the tire story cleanly. The remaining lever is on the channel side: the agent reads the PremiumContact spec, then has to find a retailer that stocks the exact size with a fitting slot. Named Hyperize offerings here: Answer Pages on "Welcher Continental-Sommerreifen in 225/45 R17 und wo mit Montage bestellen?" that route from the brand-own tire spec to the retailer-channel discovery, and Reference Pages that make Continental's named instruments (the PremiumContact vs SportContact line split, the size-fitment matrix, the retailer-availability signal) agent-readable rather than human-readable. If your brand sells through retailers and comparison portals, this is the AI test that decides whether agents reach you first or your channel does.
The proof isn't more content. It's a repeatable lift in whether agents find, verify, cite, recommend, and complete the brand journey directly — re-measured each wave.
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Tire retailers and comparison sites capture 65% of premium tires demand before Continental.
Third-Party Interception derived from AI Visibility response analysis: the retail-intermediated purchase mode applies. Premium tire orders route through tire retailers and comparison sites (Reifen.de, Tirendo, Euromaster, A.T.U) which hold inventory, fitting appointments, and checkout. The agent surfaces these retailers alongside Continental's direct PremiumContact product story.
Intermediaries Reifen.de · Tirendo · Euromaster · A.T.U
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Continental PremiumContact 7 premium summer tire in 225/45 R17 for a mid-size sedan, German market
- Close state
- config-ready
- Bottleneck
- Brand surface carries the PremiumContact spec and the tire finder; tire-retailer intermediation (Reifen.de / Tirendo / Euromaster / A.T.U) captures the order-and-fitting close.
Fairness note
Wave 11 Q2 2026 partial measurement. Single task (Continental PremiumContact 7 premium summer tire in 225/45 R17 for a mid-size sedan, config_ready close, German market). AI Visibility from the audit pass (Q2-2026, openai/perplexity/anthropic, DE language, 18/18 valid datapoints). AI Usability axis pending the agent-fleet run (scheduled for a later wave) — current Usability score reflects structural defaults for the config_ready close, not a measured per-class access profile. 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
28 May 2026
Wave · ProtocolWAVE-Q2-2026-W11-CENSUS
ars-methodology/v1.1
Wave 11 partial measurement landed. AI Visibility pass complete (18/18 valid, 0 errors). The agent-fleet run is scheduled for a later wave — Usability axis on this wave reflects structural defaults for the config_ready close. Producer-page survival of the premium-tire story confirmed by the AI Visibility response analysis; tire-retailer channel position derived from the AI Visibility response texts (Reifen.de / Tirendo / Euromaster / A.T.U dominate the order-and-fitting layer).
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 28 May 2026
AI Visibility audit · Continental Wave Q2 2026 (dispatched 2026-05-28)
Internal · Hyperize evidence
- · AI Visibility score
- · the close state reached (config_ready)
- [S2]Accessed · 28 May 2026
Agent-fleet run · Continental phase 1-4 (queued)
Internal · Hyperize evidence
- · the per-class access profile (a later wave)
- Accessed · 28 May 2026
Public · hyperize.ai
- · fairness declaration
- · Third-Party Interception framing
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Task Selection.
The fairness doctrine behind the slate above. Five failure modes, six criteria, public before each wave.
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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.
