How AI Discovery Engines Are Changing Vertical Video Promotion (and How Creators Can Benefit)

Struggling to get vertical videos discovered despite great production and steady uploads? In 2026, the problem isn't just content quality — it's how modern recommendation systems read your video as data. Holywater’s recent $22 million raise (backed by Fox) is a clear signal: the industry is moving fast toward AI-first discovery for vertical, episodic content. If creators don’t adapt metadata, thumbnails, and hooks for these AI engines, they risk being invisible no matter how good the work is.

Why Holywater’s $22M Raise Matters for Creators

On January 16, 2026, Forbes reported Holywater’s new $22 million round to scale its AI-powered vertical streaming model. That money is more than capital — it represents investor conviction that the next wave of discovery will be built on multimodal AI that understands video as searchable, linkable data.

“Holywater is positioning itself as 'the Netflix' of vertical streaming — a mobile-first platform for short episodic video.” — Forbes (Jan 16, 2026)

What the funding signals about the ecosystem

• Investor focus on AI discovery: money flows to companies using ML to index characters, scenes, and story arcs so algorithms can surface IP, not just isolated clips.
• Vertical-first storytelling is maturing: serialized microdramas and episodic shorts are now treated as discoverable properties.
• Platforms will demand richer metadata: raw video files are less important than the structured context around them — transcripts, scene labels, character IDs, and series metadata.
• New creator tools and APIs: expect more endpoints for uploading structured metadata, submitting candidate thumbnails, and hooking into recommendation testbeds.

How Modern AI Discovery Engines Work (the 2026 view)

Understanding how platforms recommend content is essential to optimizing for them. By 2026, common building blocks for AI-driven recommendation systems include:

• Multimodal embeddings: audio, visual, and text features converted into vectors so the model can compare scenes, themes, and tones across content.
• Session optimization: models that prioritize not just a single view but the entire user session — e.g., surface content that keeps people in the app longer.
• Series- and IP-level modeling: discovery models surface characters, arcs, and franchises, making series metadata more valuable than individual clip tags.
• Real-time personalization: on-device and server-side models tailor feeds to micro-segments using short-term signals (momentary engagement) and long-term interests (watch history embeddings).
• Vector databases & retrieval systems: backends (FAISS, Milvus, Pinecone, Weaviate) make semantic lookup fast — creators who provide embeddings or structured tags plug into that pipeline more easily.

Concrete Changes Creators Should Expect

• Metadata is a primary discovery signal: transcripts, scene labels, and series bibles are now first-class inputs into recommendation models.
• Thumbnails are algorithmic features: platforms test multiple visuals per viewer segment, and those visual choices feed back into the model.
• First 3–7 seconds matter more than ever: short-form retention shapes not just a single video’s reach but the creator’s future impressions in a user’s feed.
• APIs and ingestion routes will expand: expect to be able to push enriched metadata through creator platforms and CMS tools; plan to integrate.

Tactical Checklist: Optimize Metadata, Thumbnails, and Hooks for AI Recommendation Systems

The checklist below is actionable — treat it as a sprint plan you can implement in weeks, not months.

1) Metadata Optimization — Priority Steps

1. Produce clean, time-coded transcripts and captions.
   • Use speaker separation and scene timestamps. AI models use transcripts to extract entities, intents, and dialogue arcs.
   • Deliver WebVTT/SRT files with correct timestamps to platforms that accept them.
2. Use structured metadata (schema.org VideoObject and custom fields).
   • Include seriesName, episodeNumber, seasonNumber, characterList, and contentRating where applicable.
   • Provide releaseDate, language, duration, and tags aligned to the platform’s taxonomy.
3. Tag entities and relationships.
   • Tag characters, locations, recurring themes, and audio motifs. For serialized content, link episodes to a single series_id or canonical page.
   • Include alternate spellings and aliases for characters and creator names.
4. Publish semantic topics & embedding vectors.
   • Generate and store text embeddings (scene-by-scene or whole episode) using an embeddings API; attach the vector to the content record if the platform allows.
   • Where API upload of vectors isn’t possible, include rich topical tags and short textual summaries (50–200 words) that an indexer can convert to vectors.
5. Optimize titles and descriptions for clarity and signals — not SEO stuffing.
   • Place the most specific, unique info early in the title (series name or episode hook). Use descriptions to summarize story arcs and include character names and locations.
6. Provide release cadence and relationship metadata.
   • Mark episodes as part of season drops, indicate expected release cadence, and optionally provide a series bible or short descriptors of future arcs.

2) Thumbnail Optimization — A/B Ready and AI-Friendly

Thumbnails are no longer static marketing — they’re signals evaluated by both users and models.

• Design for 9:16 mobile frames: centralize faces and key elements in the vertical safe zone.
• Favor face + emotion: eyes, expression, and tension increase CTR in vertical feeds.
• High contrast and clear focal point: remove clutter. Use bold colors or lighting to separate subject from background.
• Minimal text overlays: only 2–4 words max; keep them in the top or bottom safe zone so AI OCR picks them reliably.
• Provide multiple thumbnails: supply 2–5 candidates and label them (e.g., "face-close", "action-frame", "text-overlay") so platform tests can segment by audience.
• Use AI-assisted generation with constraints: generate variants then human-vet them for authenticity and brand safety.
• Submit alt text and thumbnail captions: a short, semantic caption helps accessibility pipelines and gives another textual signal for models.

3) Hooks & Early Retention — Maximize the First 3–15 Seconds

Recommendation systems reward signals that correlate with future session value. The most direct controllable factor is early retention.

• Start with a “pattern interrupt”: unusual motion, a line of dialogue, or a visual mismatch in the first 1–2 seconds to stop scrolling.
• Deliver a clear promise in 3–7 seconds: what will happen, what the viewer will feel, or what's at stake in the scene.
• Use curiosity gaps and open loops: tease a reveal and close it within the episode to increase completion and rewatch likelihood.
• Optimize audio cues: CTR increases when sound design or a unique audio motif starts immediately. Include audible hooks in thumbnails’ first frames where possible.
• Place engagement cues strategically: early prompts for reaction (save/share) can be effective but don’t harm retention — test placement.

4) Series & IP-level Optimization — Play the Long Game

Holywater’s thesis centers on surfacing serialized IP. Treat your short-form series like a franchise:

• Create a series landing page: consolidate episodes under a canonical identifier and link to it in metadata.
• Publish a short series bible: 500–1,500 words describing characters, arcs, and themes; attach it to the series metadata or your CMS.
• Tag recurring elements consistently: use exact tag phrases for characters and motifs so models can detect patterns and cluster content.
• Stagger releases deliberately: predictable cadence helps algorithms learn engagement patterns and builds anticipation.

5) Developer & Integration Resources — Tools and APIs

Technical creators and developer partners should build an ingest pipeline that converts raw video into rich, indexable records. A minimal pipeline looks like this:

1. Ingest: upload HLS source + sidecar files to CMS or CDN.
2. Transcribe & tag: use a transcription API (AssemblyAI, Whisper-based tools, or cloud vendor speech-to-text) and NER to extract entities.
3. Scene detection & embeddings: run visual shot detection and generate embeddings per scene with Hugging Face or OpenAI embeddings.
4. Store: push vectors & metadata to a vector DB (Pinecone, Milvus, Weaviate) and keep human-readable metadata in a CMS.
5. Push to platform: use platform metadata APIs to submit VideoObject, captions, thumbnail lists, and optional embedding pointers.

Recommended toolset examples:

• Embeddings & models: OpenAI, Cohere, Hugging Face
• Vector DBs: Pinecone, Milvus, Weaviate
• Video intelligence: Google Video AI, AWS Rekognition, or open-source shot detectors
• Transcription: AssemblyAI, Rev, open-source Whisper variants
• Metadata schema: schema.org VideoObject, custom CMS fields, WebVTT/SRT for captions

Measure What Matters: Signals That Recommendation Engines Reward

To make the case for algorithmic promotion, track these KPIs consistently and use them to iterate:

• Thumbnail CTR: percent of impressions that convert to plays.
• First 15s retention: how many viewers stay through the key hook window.
• Completion rate: percent that watch to the end, especially for episodic content.
• Session time & downstream actions: whether a viewer watches more content after your video (session lift).
• Replays & save/share rates: signals of high engagement and discoverability.

Set up A/B tests for thumbnails and intros. Automate metric collection into a dashboard and prioritize changes that move session-level KPIs, not vanity metrics.

Mini Playbooks (2 Creator Examples)

Microdrama Series — Fast Turnaround Playbook

1. Upload raw clip + WebVTT captions within 24 hours of shoot.
2. Deliver 3 thumbnails: face-close, action, and mystery text overlay.
3. Tag main characters and attach a one-paragraph episode summary as the description.
4. Run a 2-week A/B: swap thumbnails every 3 days and track first-15s retention.
5. Iterate: apply winning thumbnail style across the season and submit series bible to platform metadata if available.

Educational Vertical Series — Authority & Discovery Playbook

1. Transcribe and add timestamped chapter markers (explain, demo, recap) to improve skimming behavior.
2. Provide detailed tags for skills, software names, and difficulty level; include a short summary for embedding generation.
3. Offer multiple thumbnails: speaker close-up, result snapshot, and step-by-step text callout.
4. Encourage session building: link to the next lesson in the end screen and add the series identifier to each video’s metadata.

Risks, Compliance, and Best Practices

As AI discovery grows, creators must balance optimization with responsibility:

• Be transparent about AI edits: label synthetic content, deepfakes, and AI-generated audio per platform policies and emerging regulations like the EU AI Act follow-ups.
• Protect IP and attribution: keep track of rights and credits in your metadata to avoid takedowns and to help platforms assign revenue correctly.
• Watch for filter bubbles: overly narrow metadata can trap content in small segments. Provide broader topic tags to allow serendipitous discovery.

30-Day Implementation Plan — A Practical Sprint

Follow this compact schedule to get AI-ready quickly.

1. Days 1–7: Audit current content for transcripts, thumbnails, and series metadata. Create templates for title/description formats.
2. Days 8–14: Implement a transcription + scene detection tool. Generate embeddings for 10 top-performing videos.
3. Days 15–21: Produce multiple thumbnail variants and run thumbnail A/B experiments. Add chapter markers to episodic content.
4. Days 22–30: Push enriched metadata to platform APIs (where available), monitor KPIs, and iterate on best-performing hooks.

Where This Is Headed — Predictions for Late 2026 and Beyond

• Tighter platform metadata contracts: platforms will standardize metadata fields to better aggregate cross-show IP.
• Creator-first embedding tools: expect more turnkey plugins that generate scene embeddings and automatically attach them to your uploads.
• AI-driven thumbnail orchestration: platforms may auto-create audience-personalized thumbnails on the fly — creators will curate rather than create a single image.
• More monetization tied to IP discovery: creators who provide series-level metadata and bibles will unlock licensing and sponsorship pipelines as platforms recommend their IP across surfaces.

Final Takeaways — What To Do Today

• Stop treating video as a file: treat it as an indexable dataset (transcripts, embeddings, tags, episodes).
• Optimize thumbnails and the first 7 seconds: these two levers influence both human CTR and model signals the most.
• Invest in series metadata: Holywater’s funding shows that platforms will reward creators who make their content discoverable at an IP level.

AI discovery engines are no longer experimental — they're the default. Holywater’s funding underscores that platforms will increasingly treat vertical video as serialized IP, surfaced by algorithms that read metadata as carefully as they read pixels.

Ready to act? Start with the 30-day sprint above and use this checklist as your operating procedure. If you want a ready-to-use template, download our free Metadata & Thumbnail Optimization Pack with schema snippets, thumbnail test matrices, and a transcript-to-embedding script — available at buffer.live/resources.

Call to action: Implement one metadata change and one thumbnail test this week — then watch those session-level KPIs. Share your results with the creator community and iterate: discovery in 2026 rewards action and repeatability.

Related Reading

• Temporary Retail Fixes: Fast Adhesive Solutions for Convenience Stores and Pop-Ups
• Avoiding Defensive Travel Companions: A Mini-Guide for Commuters and Adventurers
• DIY Live-Stream Production Checklist for Small Funeral Services
• In Defense of the Mega Ski Pass: Is It Right for Your Family?
• Account Takeovers and E-Signature Fraud: Preventing LinkedIn-Style Compromises in Doc Workflows