Built only from facts already on the page — not the company’s words. Shown so the gap between what was written and what could have been written is concrete rather than a number.
Five alternative headlines in five angles, a subhead, a call-to-action line, and a line-by-line edit for each of the five dimensions — written only from facts already on qdrant.tech, gated like a rating (anything with a struck word is dropped, not sold). Delivered on a private page in about a minute.
Five dimensions on one ladder — 5 · 20 · 35 · 50 · 65 · 80 · 95 — weighted clarity 30%, specificity 20%, jargon 20%, proof 15%, call to action 15%. For each: the rung the page matched, the words that drove it, and what would reach the next rung. Full rubric →
The heading includes a concrete product noun phrase: “Vector Search Engine.”
Qdrant - Vector Search Engine
Contains a measurable outcome linked to an effect (2-3x revenue).
driving 2-3x revenue.
No jargon terms present in the main claim; language remains plain.
Scored from the page as a whole; no single line was cited.
Contains a quantified outcome claim (2-3x revenue).
driving 2-3x revenue.
Primary CTA is specific and low-friction: Start Free in Qdrant Cloud.
Start Free in Qdrant Cloud
Qdrant - Vector Search EngineMain headline showing the concrete product noun.
driving 2-3x revenue.Number attached to an outcome, supporting a strong SPECIFICITY proof.
Start Free in Qdrant CloudDirect CTA aligned with the product claim.
Qdrant - Vector Search Engine High-Performance Vector Search at Scale Qdrant helps you build the AI retrieval you want. Ship high performance, full-feature vector search at any scale and with any deployment model. Start Free in Qdrant Cloud See How It Works Qdrant astronauts exploring a planetary landscape 30k+ GitHub Stars 60k+ Community Members Rust Powered SOC2 & HIPAA compliant READ CASE STUDY WHY QDRANT? Build for Production-Grade AI Search Engineered for real-time retrieval with the speed, accuracy, and scale that modern AI demands. Metadata filters code illustration Expansive Metadata Filters Store metadata in JSON and use advanced filters, such as nested , text , geo , has_vector , and more. Learn About Metadata Filters Native hybrid search illustration Native Hybrid Search (Dense + Sparse) Blend keyword and vector search in one query – use dense or sparse vectors. Supports BM25, SPLADE++, and miniCOIL. Explore Hybrid Search Multivector illustration Built-in Multivector Set new standards for relevance; make the retrieval layer more expressive, flexible, and multimodal with multiple vectors per object. See Documentation One-stage filtering illustration Efficient, One-Stage Filtering Filters are applied during HNSW traversal — no pre- or post-filtering. High recall with low latency, even under complex conditions. Reranking illustration Full-Spectrum Reranking Infuse business logic with score boosting, achieve token-level precision with late interaction models (e.g. ColB
Strongest: Clarity 95. Weakest: Clarity 95. The 2 passes agreed within 3 points. The band 90–100 is where a re-score of this unchanged page would most likely land: on 42 dry re-scores (12 Oct 2026) the median move was 6 points and one in four moved more than 10. A grade only changes when a new score clears the boundary by 5.
No word in the headline itself was judged empty. That is rare: 12 of 128 companies in the index manage it.
Static SVG. No JavaScript, no tracking.
Scored from the live homepage
on 2026-10-12 by gpt-5-nano,
2 independent passes, median taken.
Page snapshot 19848b1be308.
Rubric 7329c240 (current; what it says).
No person reviewed or edited this score. If the rubric and the score disagree, the rubric wins
and the page is re-scored — not hand-corrected.
Re-scored when the homepage text changes.