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Scored by model · 2 samples, median · No human review
Rank 89 of 128 · clearer than 26% of the index, 21% of data · Reviewed 2026-10-12
Atlan
atlan.com
· data
63
GRADE C
BAND 58–68
Ranked by what each change is worth on the composite — the gap to the next rung of the ladder times the dimension’s weight. Computed from the scores, not written by the model; the model’s sentence under each is the action.
Add a concrete noun (e.g., ‘data integration for AI’) to name a specific product category.
Attach an outcome with a measurable target (e.g., ‘reduces data prep time by 50%’).
Replace abstractions with a plain description of the product and its concrete capabilities.
All three together: 63 → 74. Every dimension one rung up: 78 (grade B). Ceiling is 95.
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 atlan.com, 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 headline uses an abstract term and does not name a concrete product noun; it relies on “Context Layer” which is not a tangible product category.
The Context Layer for AI
Next rungAdd a concrete noun (e.g., ‘data integration for AI’) to name a specific product category.
Contains quantified claim about the architecture (e.g., 80+ connectors) but does not tie to a concrete outcome metric.
80+ connectors pull context across your entire data estate
Next rungAttach an outcome with a measurable target (e.g., ‘reduces data prep time by 50%’).
Multiple abstract terms dominate the page, including “Context Layer,” “Ontology,” and “AI agents,” which obscure concrete offerings.
The Context Layer for AI
Next rungReplace abstractions with a plain description of the product and its concrete capabilities.
Mentions named enterprise customers (e.g., CME Group, Workday) but without quantified results on the page.
Managing Director, Cloud & Data Engineering CME Group
Next rungAdd a quantified customer outcome linked to a specific customer name.
Prominent CTAs exist (“Talk to Us,” “See How it Works”) that align with moving toward engagement.
Talk to Us See How it Works
Next rungPair CTA with a concrete next step like a free data-context assessment.
The Context Layer for AIHeadline uses abstract term rather than a concrete product noun.
80+ connectors pull context across your entire data estateShows a quantified feature but not an outcome.
Atlan - The Context Layer for AI Skip to main content Oct 28 Context Conference : Join leaders from Google , AstraZeneca , and Verizon who are defining the context layer. Register The Context Layer for AI Your AI doesn't know your business. Let’s fix that. Build a shared understanding of your data, your business logic, and your institutional knowledge, and make it available to every AI tool you run. Talk to Us See How it Works Interfaces & Agents Open & Portable Context Business Systems Custom Agents Salesforce Agentforce Snowflake AI assistant Vertical Agents Decagon Sierra Writer General Purpose Agents Anthropic Claude OpenAI Tools Slack Microsoft Teams Jira Context Repo Enterprise Context Layer AI-Ready Data The integrated, trusted, and AI-ready representation of an enterprise's data and knowledge assets. Connectors Data Lineage Semantics & Ontology The map of the business: its entities, metrics, and relationships. Context Agents Agent Skills Reusable, versioned, testable units of procedural knowledge. Context Engineering Studio Systems of Record Salesforce HubSpot Systems of Semantics Looker Microsoft Power BI Tableau Systems of Data Databricks Google BigQuery Google Cloud Systems of Knowledge Confluence Microsoft SharePoint Google Drive Trusted by AI-forward enterprises Mastercard Story Hubspot Story Zoom Dropbox Autodesk Nasdaq Story GitLab Virgin Media O2 Story Unilever Workday Story Elastic Story Affirm General Motors Story Easyjet Medtronic New York Life Grainger See
Strongest: Call to action 80. Weakest: Clarity 50. The 2 passes agreed within 3 points. The band 58–68 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.
Badges are issued to grades A and B. The share card for this page shows the headline with its jargon struck through; paste the URL anywhere that unfurls links.
Scored from the live homepage
on 2026-10-12 by gpt-5-nano,
2 independent passes, median taken.
Page snapshot da7135417be9.
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.