Mercedes-Benz.
Mercedes-Benz is machine-readable, but not yet transactable.
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 Mercedes-Benz, and once it arrives, transact. Talent, 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 Mercedes-Benz, free for your domain. Five agent classes, one real task, your score in 48 hours.
Bot protection blocks the simplest agents. Browser agents extract the exact price anyway.
We asked five kinds of AI agent to configure a C 180 Limousine at mercedes-benz.de. A browser agent built the full configuration, read the exact price of €42,427.31, and reached the test-drive booking from the configurator. But a plain request never got a single byte: bot protection killed the connection before the page loaded. The most capable agents get through. The simplest ones never see the car.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
Blocked somewhere on the path: Plain reader, Coding agent. A customer whose assistant runs on one of those breeds never finishes the task.
Scope. This is one model. Mercedes-Benz offers 30+ models across A-Class to S-Class, AMG, EQ, and Vans, plus after-sales, parts, and service booking.
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 Mercedes-Benz?
Mercedes-Benz 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 Mercedes-Benz'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.
Not yet measured
This AI Usability read is an estimate, not yet sourced to a clean fleet run. Mercedes-Benz's measurement is in progress; treat it as provisional until the run lands.
Evidence · 70 / 100 · estimate An estimate 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 Mercedes-Benz.
On product discovery, Mercedes-Benz is found and parsed cleanly: model pages carry clean entity markup, the configurator is research-grade. One thing stalls the lane, and it is the same thing wearing two faces. The last mile is not callable: agents reach config-ready, but the booking is built for humans, and the simplest agent classes are blocked before the page even loads. Used-car intent leaking to autoscout24 and mobile.de is a real but secondary gap, a separate intent this task did not measure.
One move changes the number: make the last mile callable. A structured configuration and booking an agent can complete, not a human-only form, and a surface the simplest agent classes can reach without being turned away at the door. The secondary lever, once the last mile holds, is recovering used-car intent with an agent-routable certified-pre-owned surface before the marketplaces frame it.
The proof isn't a cleaner configurator. It's agents completing a booking, and the simplest classes getting through the door, re-measured each wave.
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No intermediary stands between agents and Mercedes-Benz. The gap is being found, not the channel.
Measured from the Gate-1 audit: for the new-car configuration task, no intermediary surfaced as the route (0 of 18 responses). Used-car intent does route to autoscout24 and mobile.de in the market, but that is a separate job and was not part of this measurement. On the measured task, the gap is discovery and the last mile, not the channel.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Mercedes-Benz C 180 Limousine
- Close state
- config-ready
- Bottleneck
- Configurator parseable; the transactional close stalls and the simplest agents are blocked. Used-car intent leaks to marketplaces as a secondary gap.
Fairness note
Q2 2026 audit complete on a single task (C 180 Limousine, config-ready close state), scored under the public Task Selection Doctrine. AI Visibility re-audited 2026-05-22 on an unbranded informational probe (43.61) so Mercedes-Benz sits on the same unbranded discovery basis as the rest of the index; AI Usability is carried from the v6.2 baseline pending a clean fleet rerun. 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
15 May 2026
Wave · Protocola fleet wave
v1-hand-assessment
First-pass hand assessment from the a fleet wave era. Superseded by the v3 audit-derived measurement below.
- Entry · 02
18 May 2026
Wave · ProtocolWAVE-Q2-2026-PILOT
ars-methodology/v1.1
v3 audit-derived AI Visibility (27.08, 18/18 valid datapoints across 3 providers) on the C 180 Limousine task. AI Usability carried from the v6.2 baseline fleet run, pending a clean rerun.
- Entry · 03
22 May 2026
Wave · ProtocolWAVE-Q2-2026-PILOT
ars-methodology/v1.1
AI Visibility re-audited on an unbranded informational probe (43.61, was 27.08 with a branded informational in the basis). On the clean unbranded basis Mercedes surfaces strongly for premium mid-class sedan intent, so discovery is no longer the headline gap. AI Usability unchanged (browser reaches config-ready; the simplest classes are blocked). Composite recomputed two-axis to 39.52; the carried v6.2 hand-C/A and the 4-dimension composite (69.42) were removed. Confidence C, single task.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 22 May 2026
Gate-1 audit run · Mercedes-Benz Wave Q2 2026 (unbranded re-audit,)
Internal · Hyperize evidence
- · AI Visibility score (audit-derived, 18/18 valid datapoints, unbranded informational probe)
- · AI platforms queried (openai/perplexity/anthropic, 3-provider track)
- · the close state reached (config_ready)
- · task correction from E-Klasse E 220 d to C 180 Limousine
- [S2]Accessed · 16 May 2026
Hyperize fleet · v6.2 baseline (source for C/A/E)
Internal · Hyperize evidence
- · AI Usability and Evidence inputs
- · fleet test outcomes (legacy)
- [S3]Accessed · 18 May 2026
a fleet wave automotive wave (Giorgio repo, legacy reference)
Internal · Hyperize evidence
- · browser-agent phase reference (legacy)
- Accessed · 18 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.
