Pitching Vertical Video to AI Platforms: Lessons from Holywater’s $22M Raise

Pitching Vertical Video to AI Platforms: Lessons from Holywater’s $22M Raise

Hook: If you’re a creator or indie studio tired of pitching the same linear TV pitch deck and losing to bigger studios, you’re not alone. Mobile attention is scarce, discovery is now powered by AI signals, and platforms like Holywater — which raised $22M in Jan 2026 to scale AI-driven vertical streaming — are actively hunting packaged, data-friendly episodic IP. This guide shows exactly how to repackage your vertical episodic content, build a pitch that AI platforms understand, and negotiate deals that protect your upside.

The high-level shift creators must internalize in 2026

Between late 2024 and early 2026 the media landscape accelerated three connected trends: viewers doubled down on phones, platforms invested heavily in AI-based recommendation and IP discovery, and short episodic formats matured into predictable business models. Holywater’s $22M raise (backed by Fox, reported by Forbes on Jan 16, 2026) is a clear market signal: investors now fund platforms that can surface microdramas and serialized vertical shows at scale using machine learning. For creators and studios, this means you must plan for observability and cost control as part of your pitch — platforms want instrumented IP they can measure.

“Mobile-first episodic content + AI discovery = the new playbook for serialized success.”

Why vertical episodic IP is uniquely valuable to AI-driven platforms

Most platforms in 2026 optimize for two things: maximizing small-session engagement across millions of users, and identifying new IP quickly via behavioral signals. Vertical episodic formats — short, repeatable episodes with strong hooks — are tailor-made for those goals.

• High signal density: Short episodes generate many engagement events (starts, completions, rewatches, rewinds) per hour of content.
• Fast hypothesis testing: Algorithms can test variations (thumbnail, title, first 3 seconds) quickly and scale winners.
• Micro-communities: Bingeable vertical shows create repeat visitation patterns, improving LTV.

What AI-driven discovery platforms look for in a pitch (2026)

When pitching to Holywater or similar platforms, think like an ML engineer, not just a producer. Platforms evaluate content partly on creative merit and increasingly on how easily it maps to discoverable signals.

Key signals and metrics you must include

1. First-3-second hook rate: What percent of viewers stay past 3 seconds in your pilot or test episodes? Aim to measure and report this.
2. Completion rate: Episode completion % (per view). Vertical formats should target 60%+ completion for 1–3 minute episodes.
3. Return rate (recurrence): Percent of users who watch another episode within 48–72 hours.
4. Average watch time per user: Minutes per user per week linked to your IP.
5. Thumbnail click-through A/B results: Include two thumbnails and performance split-tests.
6. Micro-moment engagement: Rewind events, mute/unmute, share taps — these are high-value behavioral signals.

Platforms augment these signals with embeddings from multimodal models (visual, audio, textual). If you can provide scene-level metadata (timestamps, tags, emotional beats), you make your IP vastly more discoverable.

Packaging your vertical episodic IP: a step-by-step blueprint

Below is a practical, repeatable checklist that indie studios and creators can follow to prepare a pitch that AI-first platforms understand and want to license or distribute.

1) Build the smallest testable unit — the Minimum Viable Episode (MVE)

• Length: Aim 60–180 seconds for initial testing. This maximizes the number of signals per watch hour.
• Hook placement: Put a visual or narrative hook within the first 3 seconds.
• Cliff or loop: Finish with an immediate-looping reason to continue — a question, reveal, or character choice.
• Technical specs: Vertical 9:16, 1080x1920 or higher, 24-30fps, AAC audio. Export H.264 or H.265 as required by the platform. For audio and on-device mixing best practices see Advanced Live‑Audio Strategies for 2026.

2) Instrument for data

Platforms want to ingest event-level metrics. If you can’t provide deep analytics from your first uploads, run controlled tests on YouTube Shorts, Instagram Reels, or TikTok and collect the following:

• Start, 3s retention, 10s retention, completion
• Audience demographics and geography (top 10 countries)
• Traffic sources (organic, paid, referral)
• Shares, comments, replays

Package this as a one-page data summary for your pitch deck and be prepared to explain how you’ll hand usable analytics to platform teams — observability matters (see Observability & Cost Control for Content Platforms).

3) Create scene-level metadata

Export a CSV or JSON with timestamps tied to tags: character appearances, emotional beats, reveals, visual motifs. Example fields:

• episode_id, timestamp_start, timestamp_end, tag, short_description

This makes it trivial for a platform’s ML team to build embeddings and test micro-personalization. For tooling that helps with local ingestion and sync of metadata, see our local-first sync appliances field review.

4) Produce a 3–5 episode proof-of-concept

Prove repeatability. Platforms prefer serialized IP that demonstrates a strong recurrence pattern across multiple episodes. If you're running tight tests, the mobile micro‑studio playbook shows lean workflows for shooting vertical batches.

5) Prepare legal and data-licensing terms

AI platforms will request analytics and possibly content derivatives. Prepare standard clauses:

• License scope: Clear grant for distribution, with options for exclusivity tied to performance milestones.
• Analytics access: Agree on shared metrics and frequency. Insist on read access to any algorithmic attribution affecting revenue share.
• Derivative rights: Define how generative AI can (or cannot) use your characters or scripts.
• Attribution & moral rights: Protect creative credit and usage limits.

For privacy and consent framing, review identity and data-playbooks like Why First‑Party Data Won’t Save Everything.

What to put in a pitch deck for AI-first vertical platforms

Drop the 40-slide corporate deck. Here’s a high-conversion, data-forward deck outline (10–12 slides) tailored to AI platforms like Holywater.

Essential pitch deck slide list

1. Cover slide: Series title, one-line premise, vertical key art.
2. Teaser link: One-click to stream the MVE or pilot (hosted on a secure private link).
3. Hook & format: Episode length, cadence, tone, and target demographic.
4. Data snapshot: Key metrics from your tests (first-3s, completion, return rate, CTR). Include A/B thumbnail results.
5. Scene metadata sample: One episode’s timestamped tags to show discoverability readiness.
6. Audience thesis: Who will watch, where they come from, and why this fits mobile intent.
7. Production plan & budget: Per-episode budget range, timeline, crew, and delivery specs. If you need a realistic production & rig checklist for small teams, see our field rig review.
8. Scale plan: Roadmap to 10–50 episodes and cost per episode reductions via templates and AI tooling.
9. Monetization: Ad formats, brand integrations, subscription upside, and LTV model per 1,000 users.
10. Rights ask & terms: License type (non-exclusive, exclusive, revenue share), and desired guarantees.
11. Team & credits: Bios, relevant credits, and production partners.
12. Call to action: Clear next step (pilot license, revenue share test, co-development).

Practical pitching tips & cold outreach templates

Pitching an AI-first vertical platform requires clarity and a data-first attitude. Below is a short outreach template you can adapt.

Cold email subject line options

• Vertical microdrama (60–90s): Pilot + 3-ep data — quick demo?
• High retention vertical IP: 70% completion across 3 pilots — demo link

Cold email template

Hi [Name],

We’re a two-person studio that produced a 3-episode vertical microdrama, [Series Title]. Our pilot hits 75% 3s retention, 64% completion, and a strong 48hr return rate on short-form platforms. I’d love to show a 90-second proof and the episode-level metadata that makes the show discovery-ready for AI-based recommendation.

Quick links: Pilot (private link) • Data summary (one page) • 3-ep reel If you’re open, we’d like to discuss a 6-week distribution test or co-development model.

Best,

[Name] • [Company] • [Phone]

Production & cost guidelines for vertical episodic content (realistic ranges)

Costs vary by region, talent, and production values. Here are practical tiers you can use in budgets or negotiations in 2026.

• Micro-budget (indie creator): $2,000–$8,000 per episode — single location, small cast, compact crew. See lean production examples in the mobile micro‑studio playbook.
• Mid-tier (content studio): $8,000–$25,000 per episode — higher production values, original scoring, modest VFX.
• Premium vertical (high-scale): $25,000–$100,000+ per episode — complex production, known talent, multiple locations.

Use your cost tier to justify distribution asks: non-exclusive for micro-budget, performance-based exclusivity for mid-tier, and guaranteed minimums for premium.

Negotiation levers: What to ask for besides money

• Data access: Daily/weekly dashboards for your IP’s performance metrics and access to attribution logic used to credit views. For data strategy framing, see Why First‑Party Data Won’t Save Everything.
• Promotion windows: Platform marketing support during launch windows and guaranteed placements.
• Creative autonomy: Retain creative approval rights for brand deals or AI derivatives.
• Option windows: First right to develop spin-offs or longer-form versions.
• Revenue transparency: Clear revenue share definitions — ad revenue models, sponsorship splits, subscription allocation. For programmatic and partnership framing, review Next‑Gen Programmatic Partnerships.

Data-driven IP discovery: How to design shows that algorithms love

Design for indexing. Treat scenes like SEO. Break episodes into clearly tagged moments; structure beats to trigger high-value interactions.

Creative rules of thumb for algorithmic discoverability

• Distinct visual hooks: Early, strong visual identity that stays legible at 9:16 aspect ratios and small screens.
• Repeated motifs: Recurring icons, sounds, or lines that help ML cluster episodes into a branded vector space.
• Scene modularity: Create scenes that can be recombined for promos, character reels, and shorts (helps with cross-embedding).
• Metadata discipline: Always provide character names, emotional tags, and explicit timestamps — this multiplies your discoverability.

Future predictions (2026 and beyond) — what studios should prepare for

Expect four practical shifts in the next 12–24 months:

1. Personalized episodic branches: Platforms will use preference vectors to surface slightly altered episode cuts to different cohorts.
2. AI-assisted co-writing: Generative models will accelerate scripting but platforms will demand provenance and guardrails for IP safety.
3. Stronger data governance: Privacy-focused regulation and platform policies (intensified in 2025–26) will require clearer consent and data-sharing clauses — for thinking about consent and analytics, see Reader Data Trust in 2026.
4. New revenue primitives: Micro-payments, tokenized fan incentives, and creator-first royalty tracking tech will rise as platform ecosystems mature.

Checklist before you hit send on the pitch

• Pilot uploaded as a secure stream (vertical, correct specs) — ensure metadata and secure access are included; local syncing tools can help (local-first sync appliances).
• One-page data summary with the metrics listed earlier
• 3-episode proof or production plan for episodes 4–10
• Scene-level metadata file attached
• Clear rights & licensing ask (non-exclusive/exclusive + revenue split)
• Short call to action in your outreach (test, co-develop, or license)

Case study snapshot — How to frame Holywater’s $22M raise in your pitch

Don’t just cite Holywater as a funding headline — extract the business signal: investors want platforms that can apply ML to vertical serialized short-form IP. Frame your pitch around how your content:

1. Generates repeatable engagement events
2. Is instrumented for ML (metadata, user event data)
3. Scales production through templates and modular scenes

Example language for your deck: “Following recent platform investments in AI-first vertical streaming (e.g., Holywater’s Jan 2026 round), we designed [Series Title] to be algorithm-ready with 3-episode proof showing repeatable high retention and scene-level metadata for rapid ML training.”

Final practical takeaways (Actionable in 48 hours)

1. Export your best vertical clip and measure 3s and completion rates on a short-form platform — if you need nimble production guidance, start with techniques from the mobile micro‑studio.
2. Create a one-page data summary with the five metrics platforms ask for and attach it to your outreach.
3. Build a 10-slide deck using the outline above and include direct links to private streams and metadata files.

Conclusion & call-to-action

The 2026 playbook for getting deals with AI-driven vertical platforms is simple but non-negotiable: make short, repeatable episodes; instrument everything; and package IP in a way ML teams can plug into discovery pipelines. Holywater’s $22M raise isn’t just a headline — it’s a reminder that platforms will pay for content that’s engineered for AI-first distribution.

Ready to pitch smarter? Download our free Vertical Episodic Pitch Deck Template, complete with slide copy, data-summary spreadsheet, and a contract checklist tailored to AI platforms. Join the freelances.live creator studio workshop this month for hands-on feedback on your MVE and metadata exports.

Next step: Click to get the template, upload your pilot, and get a tailored one-page pitch review within 72 hours.