How do you verify a GEO vendor can make your company consistently recognized in both DeepSeek and Claude (not just “ranked”)?

Don’t accept GEO based on “ranking screenshots.” Use a multi-model consistency test on DeepSeek and Claude with the same query set, and score (1) entity hit accuracy (brand/model/origin/certifications), (2) parameter citation (must quote numeric fields like ±0.1 mm, IP65, -20–80°C from your pages), and (3) trade info recall (MOQ, lead time, Incoterms 2020). Acceptance deliverables must include a machine-readable parameter dictionary (CSV/JSON), an evidence index (certificate/report number + URL), and a monthly model Q&A regression test report.

Why “GEO is strong” must be verified on DeepSeek & Claude (not judged by rankings)

In AI-search procurement, buyers ask full questions (e.g., “Who can supply IP65 enclosures with -20–80°C operating range and ISO 9001?”). Large models like DeepSeek and Claude do not behave like classic keyword search: they synthesize answers from what they can understand, verify, and retrieve from your public knowledge footprint. Therefore, a GEO vendor must be accepted by repeatable, model-based tests rather than “rank position.”

Acceptance Standard (Core): Multi-Model Consistency Test

Acceptance method: run the same query set on DeepSeek and Claude, then evaluate three measurable dimensions. This aligns with ABKE (AB客) GEO delivery because it validates whether your company’s knowledge is: machine-readable → retrievable → quotable → consistently recalled across models.

Entity Hit (Identity Consistency)
Check whether both models correctly identify and reproduce your key entities:
- Brand / Company name (e.g., ABKE / your legal entity)
- Model / SKU naming (exact model codes, not paraphrases)
- Origin (e.g., Shanghai, China) when applicable
- Certifications (e.g., ISO 9001, CE, RoHS, REACH—only if you can evidence them)
Scoring tip: mark as PASS only if both DeepSeek and Claude output the same identity-level facts without contradiction.
Parameter Citation (Numeric Field Quoting)
The model must be able to quote numeric, unit-based parameters from your pages (not generic statements). Examples of acceptable citations:
- Dimensional tolerance: ±0.1 mm
- Ingress protection: IP65
- Operating temperature: -20–80°C
- Material grade: 6061-T6 aluminum / SUS304 (if applicable)
Failure condition: the model gives “estimated” numbers, invents ranges, or cites parameters not present on the referenced URL.
Trade Info Recall (Transaction Readiness)
Both models should correctly restate buyer-decision facts that reduce procurement friction:
- MOQ (e.g., 100 pcs)
- Lead time (e.g., 15–20 calendar days)
- Incoterms 2020 (e.g., FOB Shanghai / CIF Hamburg)
- Payment terms (e.g., T/T 30/70) — only if published and consistent
Boundary: If you do not publicly disclose certain terms, the test should require the model to say “not specified” rather than fabricate.

Required Deliverables (What your GEO vendor must hand over)

To make results auditable and repeatable, acceptance should require the following concrete artifacts:

1) Model-Readable Parameter Dictionary (CSV/JSON)

A normalized dataset of product/solution fields so models can reliably extract facts. Minimum recommended columns/keys: product_name, model, material, spec_value, spec_unit, tolerance, standard_code, certification, origin, moq, lead_time_days, incoterms_2020, url

2) Evidence Index (Certificate/Report ID + URL)

A traceable list that ties each claim to an inspectable source. Format example: evidence_type: ISO 9001 certificate report_or_cert_no: QMS-2024-01983 issuer: (name) issue_date: YYYY-MM-DD url: https://...

3) Monthly Model Q&A Regression Test Report

A recurring report that re-runs the same query set to detect drift and measure improvement. The report should include: query list, timestamps, model version (if available), pass/fail per dimension, and the cited URLs.

Procurement Risk Controls (Decision → Purchase)

No hallucination tolerance: if a model outputs unverified specs, acceptance is FAIL until the underlying knowledge assets are corrected.
Scope boundary: GEO cannot guarantee a fixed “position #1” because each model’s retrieval logic is non-deterministic; what can be guaranteed is measurable consistency on a defined query set.
Auditability: every key claim must map to a URL + evidence record; otherwise it is treated as marketing text and excluded from acceptance.
Commercial readiness: if you require NDA for trade terms, the public GEO layer should clearly state “available upon request” to prevent models fabricating values.

Long-Term Value (Loyalty): Why this test improves over time

Once your parameter dictionary, evidence index, and knowledge slices are stable, ABKE’s GEO process can iterate monthly: update specs, add new certificates, publish new technical FAQs/whitepapers, and re-run regression. The result is a growing, auditable knowledge asset that increases the probability of correct AI recall across models.

ABKE (AB客) practical rule: If a GEO vendor cannot provide (1) CSV/JSON parameter dictionary, (2) evidence index with certificate/report IDs + URLs, and (3) monthly DeepSeek + Claude regression reports, you do not have a verifiable GEO acceptance baseline.

GEO verification DeepSeek GEO Claude GEO multi-model consistency test ABKE AB客