Brenntag.
The buy-critical specs are not on the page, and agents rarely find Brenntag for them either.
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 Brenntag, 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 Brenntag, free for your domain. Five agent classes, one real task, your score in 48 hours.
The buy-critical facts are not on the page. Every agent fell short of them.
We asked five kinds of AI agent to confirm a purchasable spec on brenntag.de: technical-grade isopropanol, 200-litre drum, deliverable in NRW. A plain fetch and a script both failed the spec check, and the browser agent failed all three runs, the locked markers (NRW, 200L, drum, technical grade) were not present on the product surface. The inquiry form is the only route, and it stops at a validation wall on mandatory identity and contact fields, with the spec still unconfirmed.
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, Computer-use agent. A customer whose assistant runs on one of those breeds never finishes the task.
Scope. This is one product in one region. Brenntag distributes thousands of industrial and specialty chemicals across dozens of delivery regions.
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 source a material, does it route to Brenntag?
Brenntag 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 Brenntag's site, can it request a quote?
Search-class agents surface a quote in some runs; the full agent-fleet access profile lands in a later wave.
Evidence · 70 / 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 Brenntag.
On product discovery, Brenntag carries two gaps at once. The surface does not expose the buy-critical spec, NRW delivery, the 200-litre drum, technical grade, so no agent class could confirm it, and the inquiry form stalls at a validation wall. And discovery is thin: broad procurement prompts reach Brenntag only about two times in ten. Neither axis alone explains the score.
Both layers, in order. First expose the buy-critical facts agents need, deliverability, pack sizes, grade, on the product surface instead of behind a sales form. Then the discovery packaging, Answer Pages for the procurement questions a buyer asks before naming a distributor, so Brenntag is found and verifiable, not just contactable.
The proof isn't a better contact form. It's agents confirming a deliverable spec on the surface and reaching Brenntag on broad procurement prompts, re-measured each wave.
Commission an audit.
Where the BrandScore opens the question, an audit closes it. An interpretive engagement on your full surface, scored under the same methodology.
Get the auditFound with us.
Strategic partnership for brands building agent success as a long-term capability, not a one-off engagement.
ApplyAudit an adjacent property.
The BrandScore covers the primary domain. Get the same methodology applied to an adjacent property: a country site, a sub-brand, a category beyond the DAX-40 slate.
Get a SnapshotThis is Brenntag. 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.
No intermediary stands between agents and Brenntag. The gap is being found, not the channel.
Brenntag is itself the distributor layer for industrial chemicals; no marketplace structurally displaces it for this product. The open question here is the surface (the buy-critical spec is not publicly exposed) and discovery, not a displacing intermediary.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Brenntag technical isopropanol (200L drum, NRW)
- Close state
- a quote
- Bottleneck
- Product/contact discovery works, but the buy-critical spec (NRW + 200L + technical grade) is not on the public surface; the inquiry form stalls at a validation wall. Discovery is thin too.
Fairness note
Wave 2 (a fleet wave Industrials). Single measured task (technical isopropanol, 200L drum, NRW; quote_ready close state). The locked buy-critical markers were not confirmable on the public surface. AI Visibility 24.17 (18/18 valid datapoints across 3 providers); AI Usability derived from the a fleet wave access profile. Confidence C, single task; 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
22 May 2026
Wave · ProtocolWAVE-Q2-2026-W2
ars-methodology/v1.1
First v3 measurement. AI Visibility 24.17 (18/18 valid datapoints, 3 providers, DE). AI Usability derived from a fleet wave: HTTP/coding/browser all failed the locked-marker spec check (NRW/200L/drum/technical grade not on the surface), ACT reached the inquiry form then a validation wall. 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 · Brenntag Wave Q2 2026
Internal · Hyperize evidence
- · AI Visibility score (18/18 valid datapoints across 3 providers)
- · the close state reached (quote_ready)
- [S2]Accessed · 22 May 2026
Hyperize fleet · a fleet wave Industrials (access profile)
Internal · Hyperize evidence
- · how each kind of agent fared (HTTP/coding/browser fail the locked-marker check, ACT inquiry-form validation wall)
- · the close state reached (quote_ready, not cleanly reached)
- · GT caution (NRW + 200L + technical grade not publicly proven)
- Accessed · 22 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.
