Executive Summary

About 300 million knowledge workers today spend fragmented time across meetings, screenshares and ad-hoc demos, and the resulting video/audio plus screen content is largely unusable by the agents and search tools teams are deploying; that leaves practitioners and engineering teams manually summarizing, hunting for context, or training brittle templates. The practical pain is wasted time and poor agent performance when context is incomplete or unstructured, impacting both individual productivity and product support workflows. You could build a local-first pipeline that captures live screen and audio, converts them into time-stamped transcripts, UI/slide metadata, and semantic nodes in an incremental knowledge graph optimized for agent consumption, with on-device embeddings and connectors to LLMs or agent runtimes. Core components would include cross-platform capture hooks, real-time ASR and diarization, visual OCR and UI-change detection, efficient local storage and indexing, selective capture controls for privacy, and APIs that expose short-context windows and provenance to agents. The timing is favorable: the addressable market is roughly $36.0B (300M knowledge workers × $120/year average spend), market attractiveness scored 92/100 and revenue potential 88/100, driven by three correlated trends—agent proliferation, local-first privacy demands, and a steady rise in asynchronous video and remote work. Competition is medium today, but demand is accelerating as more teams require structured, privacy-respecting context to make agents reliable. To stand out you should make privacy and low-latency local processing first-class (on-device models, selective capture, and strong enterprise access controls), optimize the graph representation for incremental agent queries rather than monolithic transcripts, and provide native integrations to popular agent runtimes and collaboration platforms. Be honest about the challenges: cross-platform capture at scale, compute and storage constraints on-device, regulatory consent requirements, and the need to prove clear ROI to conservative enterprise buyers.

Executive Summary

Market Opportunity

Capture live screen + audio into a local knowledge graph for agent-ready context targets a $36.0B = 300M knowledge workers x $120/year average spend on context/agent tooling total addressable market with medium saturation and a year-over-year growth rate of 18% - growth driven by agent adoption and enterprise demand for context plumbing.

Key trends driving demand: Agent proliferation -- More teams deploy LLM-based agents that need richer, structured context to be useful.; Local-first AI -- On-device models and privacy-first tooling increase demand for local capture and processing.; Async video/remote work -- Higher volumes of video/audio meetings boost need for automated, structured capture.; Knowledge graph tooling -- Rising SDKs and graph databases lower technical cost of structured context..

Key competitors include Descript, Otter.ai, Obsidian, Scribe.

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Capture live screen + audio into a local knowledge graph for agent-ready context

Executive Summary

Capture live screen + audio into a local knowledge graph for agent-ready context

Executive Summary

Market Validation

Market Opportunity

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