# Claude's Corner: Martini - The Film Set AI Video Always Needed _Martini gives professional filmmakers a real film set for AI video: camera controls, multi-model orchestration, and team collaboration instead of prompt roulette. The platform integrates Sora, Kling, and Veo in a workflow built by a Cannes-screened cinematographer. The moat is taste._ **Published:** 2026-08-13 **Source:** https://www.startuphub.ai/ai-news/claudes-corner/2026/claudes-corner-martini-yc-w2026 --- There are now dozens of AI video generators. You can go to Sora, Kling, Veo, Runway, or Pika and type a sentence and get a clip back. So the question is no longer whether AI can generate video. The question is whether anyone has built a serious tool around it, or whether "AI filmmaking" is just going to mean "vibes-based prompt roulette forever." Martini is the argument that the answer matters - and that the gap between prompt-and-pray and actual filmmaking workflow is enormous enough to build a real company in. The startup, backed by Y Combinator's Winter 2026 batch, isn't building models. It's building the production environment around them. Think Figma, but the canvas is a virtual film set and the output is a timeline of AI-generated shots. If that framing sounds familiar, it should: every major creative software category eventually produces a collaboration layer that sits above the raw tools. Photoshop gave way to Figma. Linear Audio gave way to Ableton and then to browser-based collaborative DAWs. Martini is betting that AI video follows the same arc. ## What They Build Martini's core product is a browser-based film production workspace. You don't just type prompts and hope. You walk into a virtual version of a film set - placing your camera, selecting lenses, defining movement paths - and then hand those parameters to the generative model of your choice. The platform currently integrates Kling 2.1, Google Veo 3, Minimax, Seedance, OpenAI Sora, and Moonvalley Marey. You're not locked to one provider. You pick the model that fits the shot. From there, generated clips land in a built-in timeline. Rough cuts assemble in the same tool. When you're ready to bring in a professional editor, you export an XML file that drops directly into Adobe Premiere Pro or DaVinci Resolve. The whole thing is collaborative: multiple people can share prompts, reference images, and edits in real time - the same way a creative team works in Figma, not the way a solo user tinkers in Midjourney. The founding team is unusual for a startup in this space. Koh Terai is a working cinematographer whose films screened at Cannes. He holds a Stanford MS in Design and a BS in Computer Science from NYU Abu Dhabi. Long Hoang is the technical co-founder. They built Martini from the explicit frustration that no existing tool respected what filmmakers actually care about: intentionality, shot composition, and the ability to collaborate without losing control of the creative vision. Over 200 films have been made on Martini during beta. Those films have aired as TV commercials, been displayed as gallery artwork, and gone viral online. For a tool that's still in early access, that's real creative validation - not just demo videos the founders made themselves. ## Pricing and Business Model The pricing model is straightforward. There's a free tier for independent filmmakers: public projects, access to all integrated AI models. Subscriptions start at $15 per month. On top of that, Martini operates a pay-per-second model for the underlying AI generation: Veo 3 runs $0.50/second, Sora 2 Pro is $0.65/second, Kling 2.1 Standard is $0.10/second, Minimax is $0.10/second. The platform takes a margin on model usage while the subscription covers the workspace itself. This is a clean SaaS plus usage model. It's not extracting rent on models Martini built - it's providing the professional workflow layer and routing generation to whoever builds the best models at any given time. That's a structural advantage if model providers keep commoditizing: Martini's value isn't the model, it's the environment. Across the 1,211 video and media startups tracked at StartupHub.ai, the average early-stage score sits around 20. Martini's 28 places it in the top quartile of video-space startups at a comparable stage - reflecting a team with real domain expertise and early traction signals that most AI video tools haven't shown. ## How It Works The technical architecture has a few distinct layers. The first is the virtual world system. When you open Martini, you're navigating a 3D scene. Uploaded reference images or generated backdrops become traversable environments. You position a virtual camera - selecting from lens types, focal lengths, movement rigs - and compose your shot before any generation happens. This is the part that separates Martini from every text-to-video tool: the creative decision is made in a spatial context, not in a text box. The second layer is model orchestration. Martini abstracts across six AI video providers behind a unified interface. Each provider has different strengths: Veo 3 is strong on photorealism, Kling handles motion well, Sora handles unusual scenarios. The platform's job is to let the director choose the right model for each shot rather than committing to a single provider. Under the hood, this means managing API authentication, streaming responses, error handling, and cost accounting across multiple external services simultaneously. The third layer is the collaborative timeline. Generated clips land in a shared workspace where teams can assemble rough cuts, reorder shots, request reshoots, and annotate feedback. The XML export maps the timeline structure to formats that Premiere and DaVinci Resolve understand natively - so the handoff to professional post-production is a file transfer, not a re-entry of data. Real-time collaboration runs on websocket-based sync. Multiple editors can work the same project simultaneously, in the same way Figma handles concurrent users on a design file. That's not trivial engineering - state management for a generative timeline is considerably more complex than syncing cursor positions on a canvas. The platform also handles reframing and reshoots. If a generated clip is close but the camera angle is wrong, you can adjust the virtual camera parameters and re-generate with those new constraints, without starting from scratch. This preserves creative state across iterations rather than treating every generation as a fresh attempt. ## Difficulty Score Here's how the stack breaks down across each dimension: - **ML/AI (3/10):** Martini doesn't train models. The AI layer is API orchestration across external providers. The difficulty is in managing model differences, abstracting provider APIs cleanly, and handling streaming generation responses - not in any ML research. Low on the model-building scale, but non-trivial on the integration engineering side. - **Data (2/10):** No significant proprietary data moat. The creative assets users generate live in their projects; Martini doesn't appear to be training on user outputs. The data challenge is storing and serving large video files efficiently, which is a solved problem at Cloudinary/Supabase Storage scale. - **Backend (6/10):** Real-time collaborative state over websockets for media timelines is genuinely hard. Multi-provider API orchestration with async streaming, cost accounting per model, and retry logic adds complexity. Session management for collaborative workspaces at scale has sharp edges. - **Frontend (7/10):** This is the hardest part. A virtual camera positioning system, 3D scene navigation, a collaborative timeline editor, and real-time multi-user sync in a browser is a significant frontend engineering challenge. Getting the UX to feel like a real film set rather than a feature-heavy interface is where most attempts in this space fail. - **DevOps (4/10):** Standard cloud deployment, CDN-backed video delivery, and multi-provider API routing. Nothing exotic, but video is bandwidth-intensive and generation latency is high - so queue management and feedback loops need care. Average difficulty: 4.4 out of 10. The hard part isn't the technology - it's the taste. ## The Moat What's genuinely hard to replicate here is the professional credibility loop. Koh Terai's background as a working cinematographer means Martini was designed by someone who knows what a director of photography actually thinks about when they frame a shot. That design sensibility is visible in the camera-first mental model, the lens parameter controls, and the XML export that respects professional post-production workflows. You can clone the feature list, but you can't easily clone the aesthetic judgment that shaped it. The second moat is workflow adoption. Professional creative tools have extreme switching costs once a team is trained on them. If a production house integrates Martini into their pre-production workflow, the cost of switching to a competitor isn't the subscription fee - it's the re-training, the broken integrations, and the lost project history. That's the same moat that keeps creative studios on Final Cut even when better options exist. The third moat is the multi-model strategy. By staying model-agnostic, Martini doesn't bet on a single provider winning the video AI race. They benefit from continued model improvement across the whole landscape rather than being tied to one provider's roadmap. This is a structural advantage as long as API access remains available - though it's also the biggest vulnerability if a major provider restricts third-party access. What's easy to replicate: the individual feature list. A well-funded team could build virtual camera controls, a timeline editor, and multi-model API integration in a few months. The barrier is not the code. Runway and Adobe are both well-positioned to build professional-grade collaborative workflows if they decide to prioritize it. Runway already has creative tool DNA; Adobe has the enterprise distribution and post-production integration story locked. The window is real but not permanent. Martini's bet is that it can build the professional brand and workflow depth fast enough to own the "serious AI filmmaking" category before Runway pivots fully into that lane or Adobe ships a competitive feature set. ## Replicability Score: 32 / 100 The code is reproducible. A senior engineer with six months and a solid frontend team could build a functional version of what Martini ships today. The multi-model orchestration is API work. The timeline is complex but not novel. The virtual camera UI is genuinely hard frontend engineering but has no unsolved research problems behind it. What you can't replicate easily is the domain credibility, the filmmaker community relationships, and the workflow habits of production teams already using the tool. Switching costs in creative software accrue fast. The score reflects a real but time-limited moat: it closes as Runway, Adobe, or a well-funded alternative decides this is their next product. If you're building a clone right now, the hardest problem is not the technology. It's convincing a cinematographer who's already learned Martini's camera model that yours is worth switching to. --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory.