Tired of re-explaining workflows to AI — persistent personalized memory for workflows

Knowledge workers—roughly 200 million globally—routinely waste time re-teaching AI the same workflows, losing context between sessions, tools, and teammates; this friction falls hardest on product managers, customer success teams, developers, and solo creators who run repeatable, context-rich processes. The cost is both time and errors as handoffs and forgotten constraints force manual intervention or brittle automations. You could build a persistent, personalized workflow memory platform: a privacy-first vector store with provenance-aware RAG, SDKs and prebuilt connectors to CRMs, Slack, Git, no-code automators, and a lightweight UI for training, reviewing, and lifecycle rules (TTL, versioning, access policies). The timing is favorable—RAG and vector search make reliable long-term memory possible, composable stacks make integrations feasible, and an addressable market of 200M knowledge workers at an average willingness to pay of $300/yr implies a $60B opportunity (market score 92/100, revenue potential 88/100). To stand out, prioritize enterprise-grade privacy (client-side encryption, tenant isolation), deterministic retrieval with explainable provenance, vertical workflow templates, and a developer-first integration experience that cuts implementation from months to days. Be honest about the challenges: synchronization and model/data drift, ongoing vector storage and compute costs, regulatory/compliance complexity, and medium competition—success will likely require a tight initial vertical focus and clear ROI metrics to reach that ~$300/yr price point.