Persistent Slack memory assistant - LLM-agnostic, open-source integration

Persistent Slack memory assistant - LLM-agnostic, open-source integration targets a $600M = 200,000 Slack-paying teams x $3,000 ACV (team/year) - target marketplace of teams that would pay for Slack knowledge/assistant features total addressable market with medium saturation and a year-over-year growth rate of 15-25% (enterprise collaboration tooling and AI assistant adoption).

Key trends driving demand: Consolidation of work in messaging platforms -- Slack is the primary place decisions and context live, creating centralized opportunity for in-channel memory.; Maturing vector DB and embedding tooling -- affordable, reliable semantic search enables persistent recall without custom ML teams.; Enterprise demand for data control -- security and compliance concerns drive adoption of self-hosted or on-prem memory solutions.; LLM commoditization and model-switching -- customers want LLM-agnostic stacks so they can swap models without rebuilding knowledge layers..