Playbook
structured data for AI discovery for e-discovery platforms
A practical playbook for legal tech marketers to improve structured data—with checks, fixes, and measurement.
Why structured data matters in e-discovery
legal tech marketers cannot win AI shortlists on content alone if structured data is broken. Whether commercial pages expose accurate JSON-LD and related markup so AI systems and search engines can parse entities, products, FAQs, and organization facts.
In e-discovery, common blockers include: Buyers ask AI assistants before visiting vendor sites; Category pages are thin or JS-only; Competitors appear in ChatGPT answers for “e-discovery tools” first. Category leaders win citations when their public pages answer buyer questions more clearly than yours.
What to check
- Organization and WebSite JSON-LD consistent with on-page branding
- FAQPage or HowTo markup only where visible FAQ/HowTo content exists
- Product, SoftwareApplication, or Service types on commercial pages when accurate
- No conflicting schema that invents ratings, prices, or claims not on the page
e-discovery-specific page priorities
- Product overview — ensure this URL is crawlable HTML with facts assistants can quote when answering “best e-discovery tools for teams evaluating options”
- Security / trust — ensure this URL is crawlable HTML with facts assistants can quote when answering “best e-discovery tools for teams evaluating options”
- Comparison pages — ensure this URL is crawlable HTML with facts assistants can quote when answering “best e-discovery tools for teams evaluating options”
Fix guidance
Ship truthful JSON-LD that mirrors visible HTML, validate it, and keep entity names consistent across llms.txt and key URLs.
Deep dive: structured data for AI discovery. Industry hub: AI visibility for e-discovery platforms.
Measure with BatSignal
- Run a Visibility Scan on your e-discovery site
- Inspect the pillar tied to structured data
- Ship the prioritized fixes and copy-paste deliverables
- Re-verify within 30 days to confirm movement
Related
- e-discovery hub
- crawl access for e-discovery
- content readiness for e-discovery
- ChatGPT citations for e-discovery
- llms.txt for e-discovery
- structured data for AI discovery
- All industries
FAQ
What is structured data for e-discovery platforms?
Whether commercial pages expose accurate JSON-LD and related markup so AI systems and search engines can parse entities, products, FAQs, and organization facts. For e-discovery, this shows up when buyers ask “best e-discovery tools for teams evaluating options” and when AI crawlers attempt to fetch your commercial pages.
How do we improve structured data?
Ship truthful JSON-LD that mirrors visible HTML, validate it, and keep entity names consistent across llms.txt and key URLs. Industry-specific must-have pages include Product overview, Security / trust, Comparison pages.
How does BatSignal score this?
Content readiness / entity clarity. See the [methodology](/methodology) and related guide: /guides/json-ld-ai-discovery.