Porsche.
Porsche.com is server-rendered with embedded JSON state.
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 the commerce lane: can an agent find Porsche, and once it arrives, transact. Talent, after-sales, procurement, investor and press lanes run on different surfaces and are not yet measured.
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The same measurement as Porsche, free for your domain. Five agent classes, one real task, your score in 48 hours.
Three browser runs, three perfect prices. The 911 reads itself out.
We asked five kinds of AI agent to configure a Porsche 911 Carrera at porsche.com. The plain reader fetched the page and got the full HTML with the formatted price 136.300,00 € sitting beside the embedded JSON state — no JavaScript execution needed. The coding agent connected via CDP, waited for the SPA to render, and extracted the price from the body text in one regex match. Three browser runs each completed a 10-step configuration, every step accounted for, the final price exactly €136,300 with zero delta from the ground truth.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
Scope. This is one variant of one model. Porsche offers 911 with multiple Carrera, Targa, Turbo, GT trims, plus Taycan, Macan, Cayenne, Panamera, and the Porsche Approved used-car program.
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 configure a car, does it route to Porsche?
Porsche comes up about four times in ten. The rest of the time, an agent recommends an alternative first.
Discoverability · 18-datapoint auditOnce an agent is on Porsche's site, can it book a test drive or place an order?
Search-class agents surface config-ready in some runs; the full agent-fleet access profile lands in a later wave.
Evidence · 80 / 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.
What this means for Porsche.
On product discovery, Porsche has the surface most automotive brands aspire to. The 911 Carrera is configurable by every measured agent class, the price is correct to the cent on every run, and the choice of architecture, server-rendered React with the JSON state in the HTML, is exactly what a machine reader wants. The open question is not direction. It is breadth, whether this quality holds across 911 trims, across Taycan, Macan, Cayenne, Panamera, across the Porsche Approved used-car surface.
Porsche does not need a Reference Page to fix the configurator. It needs Reference Pages to extend the proof, one per model line, so an agent asking about a Taycan reaches the same caliber of surface as the agent that asked about the 911. The leverage is Answer Pages for the buyer questions that come before the configurator opens, and an Index that confirms the surface is consistent across the portfolio.
The proof is not a polished single model. It is the same agent-readability scored across the rest of the portfolio, 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 Porsche. The gap is being found, not the channel.
Channel position pending the Wave 4 Cody Gate-1 audit response analysis. The pre-measurement expectation, based on the ultra-premium market shape, is that own channel is strong for new-car Carrera intent; the Porsche Approved used-car program competes with mobile.de and a narrower set of premium-only marketplaces, but the measured Wave 4 task is new-car configuration, so used-car displacement falls outside this measurement.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Porsche 911 Carrera
- Close state
- config-ready
- Bottleneck
- No measured engpass on the configurator surface itself; every breed that ran reached config-ready with zero price delta. Discovery breadth and used-car routing are open questions for a future wave.
Fairness note
Wave 4 Q2 2026 audit complete on a single task (Porsche 911 Carrera, config-ready close state), scored under the public Task Selection Doctrine. AI Visibility from Cody Gate-1 on an unbranded informational probe for ultra-premium sports cars. AI Usability derived from a fleet wave phases 1-3 (ceiling + http + coding + browser-agent, 2026-03-29); the act-phase was not captured in a fleet wave for Porsche, so the full_automation row of the agent_matrix is marked pending and the derived usability is summarised from 3 measured breeds (all three succeeded). 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
29 Mar 2026
Wave · Protocola fleet wave
fleet/measured
First-pass fleet measurement: Porsche 911 Carrera (GT €136.300) across ceiling + http + coding + browser-agent breeds. SSR React with embedded JSON state, price reachable in raw HTML, browser-agent achieved 3 of 3 successes at price-perfect with the ground truth.
- Entry · 02
24 May 2026
Wave · ProtocolWAVE-Q2-2026-W4-AUTOMOTIVE
ars-methodology/v1.1
Wave 4 kickoff. Cody Gate-1 audit running on unbranded informational probe for ultra-premium sports cars. AI Usability carried from a fleet wave access profile, no act-phase data for Porsche in that wave so the full_automation row is marked pending.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 24 May 2026
Gate-1 audit run · Porsche Wave Q2 2026 (Cody, unbranded informational)
Internal · Hyperize evidence
- · AI Visibility score (audit-derived, 18 datapoints, 3 providers)
- · AI platforms queried (openai/perplexity/anthropic, 3-provider track)
- · the close state reached (config_ready)
- [S2]Accessed · 29 Mar 2026
a fleet wave automotive wave · Porsche phase 1-3 (Giorgio repo)
Internal · Hyperize evidence
- · the per-breed access profile (text/code/browser observations)
- · the SSR React + embedded JSON state finding
- · the browser-agent pass rate (3 of 3 runs, 0 percent delta)
- [S3]Accessed · 29 Mar 2026
Ground-truth ceiling run · Porsche 911 Carrera (€136.300)
Internal · Hyperize evidence
- · Ground-truth price €136.300 (Weiss uni 0€, 19/20-inch Carrera Räder Serie, model year 2026)
- · 10-step konfigurator path
- Accessed · 24 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.
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.
