Executive Summary

Knowledge workers and developers increasingly split their attention across multiple AI assistants, chat windows, code tools, and SaaS apps, and they repeatedly re-explain the same project context to each tool; this problem affects an addressable population of roughly 200 million global knowledge workers and underlies a serviceable market estimated at $60.0B (at about $300 ARPU/year). The pain is concrete: lost time, duplicated effort, and low-quality outputs when context isn’t consistently captured, structured, and shared across assistants and workflows. You could build a context-as-a-service layer that auto-extracts structured context from web apps, IDEs, docs, and meetings via pre-built connectors, converts it to compact embeddings and canonical schemas, and serves it through APIs, SDKs, and live context widgets to any assistant or app. Core product capabilities would include scalable vector DB-backed retrieval, schema reconciliation, permissioned sharing, and developer-first integrations that let teams opt in incrementally rather than retrofit their stacks. This is an attractive moment: AI assistant proliferation creates demand for a unifying context layer, embeddings and vector DB costs have fallen enough to make real-time retrieval practical, and connector commodification lowers integration cost—together they justify the $60B TAM and align with a market score of 95/100 and revenue potential of 88/100. Adoption risks remain real: enterprise privacy/compliance, reliably extracting structured facts from heterogeneous apps, and user consent/UX for context sharing will all require engineering and legal investment. To stand out you’d need to prioritize signal quality and trust—fine-grained permissioning, audit trails, on-prem/vector-hosting options, and clear ROI metrics (time saved per user, accuracy gains) rather than a feature soup—and offer developer-first SDKs and standards-based connectors to accelerate integration. Competition is medium, including platform incumbents and vertical vendors, so execution on data hygiene, privacy, and ease-of-adoption will determine whether this becomes a cross-assistant utility or just another point solution.

Market Opportunity

Never re-explain yourself to AI — auto-extract, structure & share context targets a $60.0B = 200M global knowledge workers x $300 ARPU/year (context-as-a-service) total addressable market with medium saturation and a year-over-year growth rate of 28%.

Key trends driving demand: Proliferation of AI assistants -- multiple AI tools per user create fragmented context and demand for a unified layer.; Better embeddings & vector DBs -- lower-cost, faster retrieval enables real-time, searchable context.; Connector commodification -- pre-built APIs and integration platforms reduce time to extract structured context from apps..

Key competitors include LangChain / LangSmith, LlamaIndex, Pinecone, Mem, Rewind.

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Never re-explain yourself to AI — auto-extract, structure & share context

Executive Summary

Market Validation

Market Opportunity

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