Executive Summary

Many knowledge workers (an estimated 75M globally) struggle with fragmented research and notes scattered across tabs, PDFs, chat logs and apps, and that lack of persistent, high-quality context degrades the outputs they get from LLMs and wastes time re-finding facts. Teams and individuals who rely on fast, accurate model responses — e.g., consultants, researchers, product teams — feel this pain daily and currently stitch ad-hoc solutions together. Build a lightweight capture layer that extracts highlights, metadata and embeddings into a portable vector store, with browser and app integrations that automatically surface and inject relevant contextual notes into LLM prompts and search. The product would emphasize exportability, access controls, and simple quality controls (human tagging, deduplication, relevance feedback) so context stays trustworthy and reusable. The market looks attractive now: a $12.0B addressable opportunity (75M knowledge workers × $160 ACV) driven by rapid LLM adoption and cheaper embedding/vector DB infrastructure that makes retrieval-first experiences feasible for small teams. Companies are actively seeking ways to improve LLM reliability and ROI, so early traction with niche verticals could scale. You can stand out by prioritizing privacy and portability (team-hosted or exportable stores), tight in-context injection into popular LLM workflows, and tooling for curation to keep context high-quality; however, expect uphill work on onboarding, data hygiene, and integration maintenance in a medium-competition landscape. Focus initial go-to-market on specific high-value verticals and integrations to prove ROI before broad expansion.

Market Opportunity

Capture scattered research and inject contextual notes into LLM workflows targets a $12.0B = 75M knowledge workers × $160 ACV total addressable market with medium saturation and a year-over-year growth rate of 15% YoY — AI productivity and knowledge management tools expanding (source: McKinsey/industry reports 2023-2024).

Key trends driving demand: Trend — Rapid LLM adoption by knowledge workers increases demand for persistent, high-quality context that improves model outputs.; Trend — Embedding-based retrieval and vector DBs make fast semantic search practical and affordable for small products, enabling new retrieval-first experiences.; Trend — Increased focus on privacy and data ownership means users and teams prefer portable, exportable context stores over vendor-locked solutions.; Trend — Growth of composable AI tooling and browser/IDE extensions creates distribution channels for cross-app context injection..

Key competitors include Mem, Notion, Obsidian, Tana.

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Capture scattered research and inject contextual notes into LLM workflows

Executive Summary

Market Validation

Market Opportunity

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