SubQ Snap, a long-context decision model for enterprise work
The Hook
“Today, we're announcing the first long-context decision model that can actually handle real enterprise work.”
Product Reveal
“Powered by Subquadratic Sparse Attention, SubQ Snap's multimodal context window is 12 times larger than Jev's.”
Product Reveal
“And Snap leads across the decision model benchmarks that matter for enterprises.”
Problem Agitation
“It isn't just better. It is a different type of decision model. Here's why. Decision models become exciting when they can take real work away from frontier LLMs.”
Problem Agitation
“With a context window of 8,000 to 32,000 tokens simply can't handle important decisions for enterprises with multi-million line codebases or knowledge bases of hundred-page documents.”
Product Reveal
“That's what Snap was trained for. Snap was trained for agentic decisions where it is purpose-built to tackle long-horizon agentic workflows.”
Feature Teaser
“It is able to dramatically reduce cost and latency within tasks like finding insights and contradictions in large documents, building a multimodal recommendation system, curating context within coding agents, or deciding if an item is a hot dog or is not a hot dog.”
Feature Teaser
“Snap does this by taking the data-intensive and structured decisions while the LLM handles workflow steps that are open-ended. It can even one-shot long-context problems on its own, like temporal reasoning or compliance screening.”
Call to Action
“This represents a fundamental shift to the AI stack for enterprises. Go to subq.ai to learn more.”
Synthesizable in Remotion · key components of the editorial-technical-launch template.
Serif title text struck through horizontally by an animated thin line while tracking slightly.
Use interpolate on frame to animate scaleX of an absolute positioned line div from 0 to 1 over 15 frames.
Workflow blocks move from an LLM container into a SubQ Snap container with updating cost/latency bars.
Map task elements with spring-interpolated translateY/X from container A to B while driving bar width states.
Side-by-side vertical bar chart with percentage labels expanding upwards with elastic easing.
Drive height percentage with remotion spring using mass 0.5, damping 12, followed by opacity fade-in of labels.
Purpose. Capture immediate enterprise attention with a disruptive model claim.
Execution. Founder address overlaid with sharp editorial title blocks.
Purpose. Establish immediate technical credibility.
Execution. 12x typography highlight paired with empirical benchmark tables.
Purpose. Explain how agentic workflow offloading works across real UI tasks.
Execution. Fast-paced UI demos, animated task migration, and cost/latency comparison graphs.
Purpose. Direct users to adoption portal.
Execution. Clean dark screen with high-contrast URL and branded logo reveal.
Objective
Launch an ultra-fast developer database indexing engine.How the recipe adapts
Replace decision benchmarks with latency query throughput charts, replace document processing demos with code search terminals, and preserve the rapid founder cut-in pacing.Scene durations (6 scenes, 76s)
Key Remotion components
Introducing Griffin, the first AI to pass the video Turing test
The most emotive and controllable TTS model

Always-on agents built to handle everything