MTU Aero Engines.
On MTU's own pages, everything an agent needs is there in plain HTML: the PW1100G-JM, its A320neo application, the Hannover maintenance operation and a reachable B2B contact.
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 MTU Aero Engines, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on MTU Aero Engines'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 MTU Aero Engines, free for your domain. Five agent classes, one real task, your score in 48 hours.
The MTU site passes every agent test we ran. Discovery is the open question.
We asked three AI providers (OpenAI, Perplexity, Anthropic) 18 questions in German: who services PW1100G engines, how MTU compares with Lufthansa Technik and Pratt & Whitney, and how to request a quote from MTU Maintenance Hannover. We also sent AI agents directly to mtu.de, to the maintenance pages and the PW1100G-JM partnership page.
The agents that read the site succeeded. The engine family page names the PW1100G-JM for the A320neo. The plain HTML confirms every detail of the service, on MTU's own pages. The path to a quote request is reachable.
Two agent classes, browser-based and fully autonomous, were not tested this time. That was a limit of our test setup, not the brand. A follow-up measurement is scheduled.
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 a supplier, does it route to MTU Aero Engines?
MTU Aero Engines comes up just over half the time. The rest of the time, an agent recommends an alternative first.
Discoverability · 18-datapoint auditOnce an agent is on MTU Aero Engines's site, can it reach an RFQ pathway for the PW1100G-JM MRO service?
Every kind of agent gets through, and reaches a quote.
Every agent class reaches a quote, no path past the quote was tested · breakdown aboveEvidence · 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 MTU Aero Engines and prepare the application. Same brand, second surface: the career portal.
Application-ready for agents.
Both non-browser agent classes — plain HTTP fetch and structured-data extraction — reads the full posting and its requirements directly from mtu.de's server-rendered HTML and JobPosting JSON-LD; the application entry then hands off to a SuccessFactors career portal that requires account creation or login and a mandatory CV before a candidate can proceed.
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 covers only GET/HEAD access to the job-posting page and the apply-entry landing page's rendered configuration (login requirement, required/optional artefacts). No account was created, no form was filled, and no application was submitted — the actual submission step through SuccessFactors was not tested.
Fairness note
MTU Aero Engines publishes real, server-rendered job postings with structured data on its own career site, and both tested agent classes read the frozen posting (Req 630, SAP Full-stack Developer GTS, Munich, re-locked 2 July 2026) in full; browser-automation agents were not tested in this round. The SuccessFactors login and mandatory CV at the application step are documented as facts of the process and carry no score deduction; no application was submitted and no personal data was used. The score reflects how deep the tested agent classes get and how many classes were measured, not a judgment of MTU's hiring compliance.
What this means for MTU Aero Engines.
MTU is not lacking surface quality; it lacks discovery share. The PW1100G-JM chain reads cleanly for agents, and the spec-fidelity question resolved in MTU's favour: both measured classes disambiguate the GTF program from V2500, GP7200 and GE9X on brand-owned pages. But the unbranded engine-MRO question reaches MTU only about half the time, and the RFQ pathway still routes through a cross-program Maintenance contact form rather than a per-program entry point.
Answer surfaces that own the unbranded MRO questions (shop-visit decisions, GTF on-wing support, A320neo engine service) and cite the Hannover capability as evidence, so the agent arrives at mtu.de before it settles a shortlist. At the close: a PW1100G-JM-specific RFQ entry that identifies the engine and airframe combination directly, instead of the cross-program contact form.
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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Get a SnapshotThis is MTU Aero Engines. What about your brand?
You just saw how an AI agent treats a DAX 40 brand.
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.
MTU Aero Engines owns its channel. Agents arrive directly.
Commercial aero-engine MRO closes via direct B2B sales; Lufthansa Technik, Pratt & Whitney EngineWise and AFI KLM compete the same way, without a marketplace platform. No intermediary captures the close. The close stays on mtu.de.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
MTU Maintenance Hannover · PW1100G-JM (GTF) engine MRO service for A320neo
- Close state
- a quote
- Bottleneck
- Discovery, not the surface: the unbranded MRO question reaches MTU about half the time, while the locked chain resolves fully once an agent is on mtu.de.
Fairness note
Wave 8 Q2 2026 partial measurement on a single task (MTU Maintenance Hannover · PW1100G-JM MRO service, quote_ready close, GTF engine for A320neo locked SKU). AI Visibility from the audit pass (52.50, 18/18 valid datapoints across openai/perplexity/anthropic, DE language). AI Usability from a fleet wave Phase 2 only (HTTP + coding agents, both success; Phase 3 browser + Phase 4 ACT infrastructure-blocked in this wave: fleet browser runtime unavailable, not a brand result; re-run scheduled). The browser and full_automation rows of the agent_matrix are marked pending. 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
26 May 2026
Wave · ProtocolWAVE-Q2-2026-W8-AEROSPACE-DEFENSE
ars-methodology/v1.1
Wave 8 dispatch. AI Visibility audit and agent-fleet ceiling pulls launched 2026-05-26. Pre-measurement state: yaml seeded with frozen task (MTU Maintenance Hannover · PW1100G-JM MRO for A320neo), close-state quote_ready, locked spec markers per spec-fidelity rule (PW1100G-JM vs V2500/GE9X, A320neo vs A330/787, MTU 18% stake).
- Entry · 02
13 Jun 2026
Wave · ProtocolWAVE-Q2-2026-W8-AEROSPACE-DEFENSE
ars-methodology/v1.1
Wave 8 partial measurement landed. AI Visibility pass complete (D 52.50, 18/18 valid). a fleet wave Phase 1 ceiling + Phase 2 HTTP+coding complete, both classes success: the full locked chain (PW1100G-JM, A320neo application, Hannover MRO, quote pathway) resolves on brand-owned pages over plain HTTP. Phase 3 browser + Phase 4 ACT not executed in this wave (fleet browser runtime infrastructure-blocked, not a brand result; marked pending in agent_matrix, re-run scheduled). Brand flipped to scored at partial-FLEET.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 26 May 2026
AI Visibility audit · MTU Aero Engines Wave Q2 2026 (dispatched 2026-05-26)
Internal · Hyperize evidence
- · AI Visibility score
- · the close state reached (quote_ready)
- [S2]Accessed · 26 May 2026
Agent-fleet run · aerospace + defence · MTU phase 1-4 (dispatched 2026-05-26)
Internal · Hyperize evidence
- · the per-class access profile (Phase 1-4 pending)
- Accessed · 26 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.
Read ChallengeDisagree with this score.
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Open Surface Run · additive measurement. The Hyperize-selected slate stays frozen; your task gets the same methodology.
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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.
