Keyframe Labs

Competitive Intelligence & Product Roadmap

Turn agents into lifelike real-time video calls.

Company Overview

Keyframe Labs is a video-agent API that renders AI agents as lifelike video-call personas. Serving enterprise training, support, recruiting, telehealth, and consumer AI buyers where no named customers are public.

Latest Intel

Zeitgeist tracks private signals to determine where the company is heading strategically.

What They're Building

The company's public product roadmap & what they're committed to building.

persona-1.5-live

The current live persona model adds emotion control and replaces the older live model for better lip sync and expression.

Hosted agent calls

Customers can bring an existing OpenAI Realtime, ElevenLabs, or LiveKit agent while Keyframe hosts the video-call layer.

Self-managed SDK

Teams can keep agent credentials in their own stack and use Keyframe browser primitives for custom embeds.

Widget deployment

Nontechnical teams can design the agent flow in Keyframe and embed the video persona through a no-code widget.

Custom personas

Enterprise buyers can request custom personas with volume commits, higher concurrency, and SLA terms.

Competitors

Tavus:

Tavus sells a conversational video interface API with real-time video agents and swappable voice, LLM, and knowledge components.

Simli:

Simli focuses on real-time streaming avatars for AI systems.

D-ID:

D-ID offers interactive AI agents and avatar video tools with a broader creative and enterprise surface.

HeyGen:

HeyGen pairs avatar video creation with LiveAvatar and API products that overlap with real-time persona use cases.

Keyframe Labs

's Moat:

Technical infrastructure is the clearest moat path: low-cost real-time photoreal video rendering could compound if custom persona assets and latency tuning become hard to replace.

How They're Leveraging AI

AI Use Overview:

Persona-1 uses a two-stage flow-based image transformer that maps audio embeddings into compressed image latents, making the video layer the core model rather than a wrapper on an LLM.

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