Airbus.
AI agents read the H145 pages and reach the live sales form, but they never capture the full specification in one pass.
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 Airbus, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on Airbus'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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AI agents find the H145 and the sales form, but miss key details.
We tested one buying scenario: an AI agent shortlists a twin-engine medium helicopter for an emergency medical fleet and requests a quote for the Airbus H145. We ran 18 measurements across three AI providers, in German: an unbranded search, a comparison with Bell and Leonardo, and a direct quote request. Agents also read airbus.com directly.
They found the H145, its twin engines, the 8-seat layout, the 3.8-tonne take-off weight, and a path to the sales contact form. Two facts never came through in one pass: final assembly in Donauwörth and the Cat-A performance wording.
Browser-based and fully autonomous agents were not measured this round. The cause was our test setup, not Airbus. 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 Airbus?
Airbus 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 Airbus's site, can it reach an RFQ pathway for the H145 twin-engine medium configuration?
Plain reader and Coding agent reach a quote in some runs; other agent classes do not.
text / code-class agents reach a quote only in some runs; agents are blocked, 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 Airbus and prepare the application. Same brand, second surface: the career portal.
Readable posting, unresolved apply step.
The Workday-hosted posting page serves the full job description (title, requisition ID, location, requirements) directly in server-rendered HTML via JSON-LD, readable by any HTTP-only agent class, but the application entry hydrates entirely client-side, so login requirements and required artifacts are unreadable without a JS-executing browser.
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 covers only GET/HEAD requests against the posting page and the apply-entry URL. No form was filled, no login was attempted, and no application was ever submitted. The apply page's login requirement and artifact list (CV, cover letter) remain unverified pending a Phase C (Chrome) pass.
Fairness note
This measurement was taken on Airbus's group-wide Workday careers site against a live, entry-level software role in Backnang whose employer of record is Tesat-Spacecom, an Airbus subsidiary. The posting page itself proved fully machine-readable, and the score reflects only that the application entry cannot be read without a JavaScript-capable browser, a behavior common to Workday-hosted career sites. No form was filled, no login was attempted, and no application was submitted; where a login is required, we record it as a fact, not as a deficiency.
What this means for Airbus.
Airbus is not lacking product depth; it lacks the agent-readable pinning that turns a deep human surface into a quotable one. The H145 page carries the spec a buyer needs, and the sales form is real. But an agent assembling an RFQ cannot anchor the Donauwörth assembly reference or Cat-A performance wording from the page in one pass, and the regional-sales pathway still assumes a human picking a country before a contact appears.
An answer surface that pins the locked H145 facts in extractable form: variant, MTOW, assembly site, certification wording. And an RFQ pathway reachable from the product page without the country-selector detour. Citeable evidence beats deep PDFs here; the agent quotes what it can anchor.
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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No intermediary stands between agents and Airbus. The gap is being found, not the channel.
Aerospace primes close via direct-sales RFQ; no marketplace intermediary captures the close. Procurement platforms such as defence procurement databases coordinate tenders but are not commercial RFQ marketplaces. The close stays on airbus.com.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Airbus H145 (twin-engine medium helicopter, 8 seats, EMS/VIP/Para-Public, MTOW 3.8t)
- Close state
- a quote
- Bottleneck
- The locked spec does not pin: the Donauwörth assembly reference and Cat-A wording resolve nowhere in one extraction chain, and the RFQ path runs through a human country selector.
Fairness note
Wave 8 Q2 2026 partial measurement on a single task (Airbus H145, quote_ready close, twin-engine medium helicopter, 8 passenger seats / MTOW 3.8t / final assembly Donauwörth locked variant). AI Visibility from the audit pass (43.94, 18/18 valid datapoints across openai/perplexity/anthropic, DE language). AI Usability from a fleet wave Phase 2 only (HTTP + coding agents; 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 (Airbus H145 twin-engine medium helicopter, 8-seat / MTOW 3.8t / Donauwörth), close-state quote_ready, locked spec markers per spec-fidelity rule.
- 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 43.94, 18/18 valid). a fleet wave Phase 1 ceiling + Phase 2 HTTP+coding complete, both classes partial: H145 identity, twin-engine framing, 8-seat layout, MTOW and the contact-sales path confirm; the Donauwörth and Cat-A wording never pins in one extraction chain. 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 · Airbus 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 · Airbus 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.
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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.
