Executive Summary

Many engineering teams lose continuity when switching between AI assistants or sessions: context about why a design was chosen, previous debugging steps, and relevant PR discussion is scattered across repos, tickets, and chat, forcing repeated explanations and costing time for roughly 2M developer teams globally. This pain is acute for mid-to-large teams where onboarding and context-switching can translate to measurable rework and slower delivery. You could build a unified contextual store that continuously indexes code, PRs, issue threads, design docs and assistant interactions into embeddings with fast vector search, plus connectors to Git, Jira, Slack and IDEs and org-controlled deployment options (local-first or managed). The product would offer automatic summarization, versioned memory, permissioned access controls, and APIs for assistants to persist and retrieve session memory across tools. This is an attractive market right now because AI-assistant adoption among developers is rising, embeddings and vector search are becoming cost-effective, and the TAM is roughly $6.0B (2M teams × $3K ACV) with a market score of 88/100 and revenue potential at 85/100. Competition is medium, meaning there’s room for a focused, well-executed offering. The clearest competitive edge is combining low-latency, permissioned local-first storage with deep IDE/assistant integrations and strong governance to meet privacy needs, but you’ll need to solve hard engineering problems around continuous indexing, data freshness, and cross-tool semantics to make the experience seamless.

Market Opportunity

Preserve developer project memory across AI sessions with unified contextual store targets a $6.0B = 2M developer teams × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% YoY — driven by AI developer tooling adoption (sources: GitHub Octoverse, Stack Overflow developer trends).

Key trends driving demand: Rising AI-assistant adoption — more developers use AI tools daily, increasing demand for persistent context across sessions.; Embeddings and vector search maturity — lower latency and cost enable continuous indexing and fast retrieval for developer artifacts.; Local-first and privacy-first demand — teams prefer solutions that keep sensitive code and decision logs under org control, opening an opportunity for permissioned memory stores..

Key competitors include Sourcegraph Cody, Mem (mem.ai), Rewind, DIY RAG (Notion/Obsidian + embeddings).

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Preserve developer project memory across AI sessions with unified contextual store

Executive Summary

Market Validation

Market Opportunity

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