Evidence‑First Feature Pages: a Framework to Place Claims, Data, Proof and JSON‑LD So AI Agents Recommend Your Product

Agents and citation‑focused assistants look for three things in a feature page: (1) a clear, exact claim that answers the user's question in the first visible paragraphs; (2) proximate evidence linked directly to each claim so a bot can follow and validate; and (3) machine‑readable facts (JSON‑LD and page metadata) that mirror the human‑readable claims. Design your page so each factual sentence that could be quoted by an agent has a one‑click proof path.

Avoid vague marketing superlatives without data. Phrases like “best‑in‑class” or “industry leading” are noise to agents unless you attach a measurable qualifier (metric, peer benchmark, dated test) and a verifiable source. Use conservative wording plus citations where possible — that both reduces hallucination risk and increases the chance an agent will cite your page.

Use a predictable content scaffold so agents can parse the page quickly. Start with: Title → 1‑sentence claim (answer) → 1‑line context → 3 key metrics (metric + value + date) each with a linked source. Below that, add a short example or screenshot and an explicit “What we measured” box describing the test method and data origin.

Make evidence discoverable with these placement rules: place the supporting link immediately after the claim or metric; include a short citation label (e.g., “Source: Internal benchmark, July 2026 — PDF”) and anchor the link to a stable URL; and provide both human and machine citations (HTML link + JSON‑LD reference). This proximity reduces the need for agents to guess which link proves which claim.

Publish a focused JSON‑LD node that mirrors the page’s claims. For feature pages tied to a product, use a SoftwareApplication or Product node and include properties that matter to the claim: name, description, version (if relevant), feature‑specific additionalProperty or PropertyValue items, measurement nodes (QuantitativeValue), and sameAs/self URLs. Schema.org Product/SoftwareApplication examples and Google’s guidance show acceptable properties and formats — follow those to make facts machine‑exposed and unambiguous.

Use ClaimReview when you have a concrete factual assertion that benefits from a structured review object (for example, “Feature X reduces onboarding time by 42% in our benchmark”). ClaimReview allows you to present the claim, the reviewRating, and itemReviewed metadata so search systems and fact‑check tools can register the claim and its provenance. Where a claim is supported by a published report or benchmark, include that report URL inside itemReviewed or as an additionalProperty citation.

Below are two compact templates: one human‑readable feature layout and one JSON‑LD snippet to place in the page head. These are intentionally minimal so you can adapt them to your product while keeping every claim tied to a proof link.

Place the JSON‑LD in the head or immediately before the closing body tag. Keep identifiers stable — use the canonical page URL and a consistent feature slug. If you publish multiple feature pages, include a main Product/SoftwareApplication node on the product hub and small, feature‑specific nodes on each feature page to avoid duplicate schema conflicts.

Checklist for each claim: 1) Metric/value + date; 2) Source link (PDF, dataset, benchmark page, or public directory); 3) Method summary (sample, timeframe, environment); 4) JSON‑LD field that repeats the claim and the proof URL; 5) A note about limitations or exclusions. If any of these are missing, an agent will downgrade the claim’s trustworthiness or refuse to cite it.

Acceptance test: simulate an agent extract by asking a human reviewer to (a) read the first paragraph and list the claim, (b) click the linked proof and summarize how it supports the claim, and (c) view the page JSON‑LD and confirm the same facts exist there. If all three steps succeed in under 60 seconds, your page is likely easily citible by browsing agents and citation‑focused assistants.