In one line: High-level primer for decision-makers. This guide shows what matters, how it works, and the safe steps you can ship—without revealing proprietary methods.
What you'll learn
- What assistants and AI Overviews look for before citing a source
- How to structure entities, content, and schema for clarity
- Safe, high-ROI actions to ship this quarter
- Measurement that ties improvements to prompts and revenue
Why this matters now
Why this matters now — assistants and AI Overviews shape discovery and conversions. In practice, you’ll get farther by clarifying scope, aligning teams, and expressing proof in ways machines can parse. The goal isn’t to trick models; it’s to make reality legible and consistent across your site and the wider web. Treat every key claim as a data point that must be corroborated. Keep templates clean, reduce ambiguity, and favor simple patterns over clever hacks. When in doubt, choose clarity: define entities, relationships, and evidence explicitly. These steps also help classic SEO by reducing crawl waste and content duplication.
Credibility signals — consistency, corroboration, and clarity. In practice, you’ll get farther by clarifying scope, aligning teams, and expressing proof in ways machines can parse. The goal isn’t to trick models; it’s to make reality legible and consistent across your site and the wider web. Treat every key claim as a data point that must be corroborated. Keep templates clean, reduce ambiguity, and favor simple patterns over clever hacks. When in doubt, choose clarity: define entities, relationships, and evidence explicitly.
Compounding effects — small, iterative fixes that reinforce each other. In practice, you’ll get farther by clarifying scope, aligning teams, and expressing proof in ways machines can parse. The goal isn’t to trick models; it’s to make reality legible and consistent across your site and the wider web. Treat every key claim as a data point that must be corroborated. Keep templates clean, reduce ambiguity, and favor simple patterns over clever hacks.
How it works (high level)
- Define your primary entities and their relationships (Organization, Products/Services, People, Topics).
- Express those entities in clean templates with JSON-LD and unambiguous copy.
- Corroborate claims with on-site proof and aligned off-site references.
- Map prompts to pages and add intent-led FAQs with internal links.
- Measure prompt coverage, citation rate, and source mix; iterate biweekly.
The safe playbook (ship this in two sprints)
- Sprint 1: baseline audit of entities, schema hygiene, internal links, and thin content.
- Ship: normalize titles/meta, add/repair JSON-LD, fix canonicals, and create one hub + 5 FAQs.
- Sprint 2: expand FAQs, strengthen proof pages, and connect hubs to product/use-case pages.
- Governance: add a release checklist and measurement cadence; avoid schema bloat.
Anti-patterns to avoid
- Publishing proprietary methods that competitors can easily mirror.
- Over-optimizing with irrelevant schema types or keyword stuffing.
- Launching a dozen low-quality pages instead of one strong hub with supporting detail.
- Ignoring off-site corroboration and brand consistency.
A quick comparison of signals
| Aspect | Evidence | Action |
|---|---|---|
| Signal | Evidence Example | How to Ship |
| Entity clarity | Consistent org/product descriptions | Standardize names/attributes; centralize JSON-LD |
| Corroboration | Press, docs, directories | Link out; align facts and dates |
| Freshness | Recent updates on key pages | Changelog, release notes, dated FAQs |
| Experience | Author bios, case notes | Add bios; safe, verifiable proof points |
Measurement and iteration
Measurement — pick few metrics, review biweekly. In practice, you’ll get farther by clarifying scope, aligning teams, and expressing proof in ways machines can parse. The goal isn’t to trick models; it’s to make reality legible and consistent across your site and the wider web. Treat every key claim as a data point that must be corroborated. Keep templates clean, reduce ambiguity, and favor simple patterns over clever hacks. When in doubt, choose clarity: define entities, relationships, and evidence explicitly.
- Prompts tracked → coverage and trend
- Citation rate → by prompt and cluster
- Source mix → authority, freshness, alignment
- Outcomes → assisted conversions and qualified pipeline
Iteration — prioritize near-miss prompts and tighten clarity. In practice, you’ll get farther by clarifying scope, aligning teams, and expressing proof in ways machines can parse. The goal isn’t to trick models; it’s to make reality legible and consistent across your site and the wider web. Treat every key claim as a data point that must be corroborated. Keep templates clean, reduce ambiguity, and favor simple patterns over clever hacks.
Mini-scenarios (directional and safe)
SaaS: Clarify use-case pages and integrations; add FAQs per task; first citations arrive for 8/40 prompts.
Ecommerce: Normalize product attributes and availability; assistants cite curated collection pages.
Services: Publish process/coverage proofs and author bios; improved trust yields Overview inclusion.
Next step: Start with an AISO Audit
Ongoing visibility: AISO Monitor
Turn insights into compounding traffic: AISO Optimize
Questions? → Contact us