Multimodal Marketing: Feeding Gen-AI Copilots Beyond Text

The RICH-MEDIA framework is the checklist I run before publishing any non-text asset:

• R — Reliable metadata: Descriptive alt text, EXIF keywords, JSON-LD ImageObject/VideoObject blocks.
• I — Intent tagging: Pair every asset to a specific target query or user intent so models map it to the right questions.
• C — Content layering: Provide multiple resolutions (thumbnail, preview, full) plus transcripts and captions—LLMs love redundancy.
• H — Human context: Add on-screen titles, speaker names, brand watermark; these survive in frame hashes used by vision models.
• M — Machine-readable embeds: Use WebP/AVIF with <source> fallbacks, HLS for video, FLAC for audio.
• E — Engagement markers: Chapters, hotspots, CTAs that generate on-video events you can feed back as training signals.
• D — Distribution signals: Submit to sitemaps (image, video, podcast) and surface in llms.txt.
• I — Integrity proofs: Optional C2PA or watermarking to survive authenticity checks in future model pipelines.
• A — Availability SLA: Host on a CDN with >99.9 % uptime; LLM crawlers quietly drop flaky assets.

Google Vision, CLIP, and Gemini all tokenize image concepts first, then fallback to surrounding text. Here’s how to maximize that primary signal:

• Descriptive alt text: 80-120 characters, lead with the entity, include a data point if relevant (e.g., “Marketing funnel diagram showing 37 % drop-off at consideration stage”).
• Structured data: Add an ImageObject with author, license, contentUrl, and especially keywords.
• Vector embedding sitemap: Store CLIP embeddings in a nearest-neighbor index and expose to models via an /embeds.json endpoint—Perplexity and Brave now crawl these.
• Format strategy: Use WebP for photos, SVG for diagrams, AVIF only if you have proper fallback; broken formats score negative freshness points.
• Size cadence: 512 × 512 thumb, 1024 × 1024 mid, and original; the first two are widely used in common vision-cache layers.

LLMs treat video as time-stamped text + vision frames. Give them both:

• Captions & transcripts: Upload SRT/VTT captions and a full transcript; include speaker labels for multi-voice content.
• Chapter markers: Segment every 60-120 seconds with keyword-rich titles—these become anchor points in Gemini answers.
• YouTube + self-hosted: Host on YouTube (fast crawl) and duplicate on your CDN with a VideoObject schema; models will resolve the canonical.
• Thumbnail SEO: Create a 1280 × 720 cover with readable text; vision models parse the title directly from the image.
• Embed policy: Allow crossorigin and ensure player.js doesn’t block headless clients.

Audio is the sleeper channel—OpenAI’s Whisper and Meta’s MMS crawl millions of podcast minutes each day:

• Transcript quality: Provide a human-edited transcript in plain text and JSON-LD Article format.
• RSS enrichment: Add <itunes:keywords>, episode summaries, and <content:encoded> with full show notes.
• Audio snippets: Create 30-90 s highlight clips and expose via link rel="preview"; Gemini surfaces these directly in results.
• Loudness normalization: −16 LUFS mono; Whisper accuracy declines ~7 % when files exceed −12 LUFS.
• Open licensing: If feasible, mark episodes CC-BY. LLMs prioritize content they can legally reuse.