Screen & Audio Context Knowledge Graph for AI Agents

Users struggle to feed diverse context (videos, calls, chats) to their AI agents efficiently. Sinain captures screen and audio into a local knowledge graph that can be shared with agents via MCP or peer-to-peer. Target users are AI engineers and teams running agent fleets.

The explosion of agentic workflows in 2024–2025 has exposed a real gap: agents are powerful but context-starved, and screen/audio data remains largely untapped as a structured input source. Mem0 and similar memory layers touch adjacent territory but focus on text, leaving multimodal capture effectively unclaimed by any clear incumbent. The $2k–10k/mo revenue band is plausible if sold as a team-tier tool to agent-heavy engineering shops, where even modest productivity gains justify the spend, though it likely requires a usage-based pricing model to land initial contracts. The biggest risk is adoption friction — capturing screen and audio locally while maintaining a queryable knowledge graph is technically non-trivial, and most AI engineers will default to simpler RAG pipelines rather than adopt new infrastructure they have to trust, maintain, and explain to their security team.