Manage AI memory to prevent context pollution with selective context hygiene

Manage AI memory to prevent context pollution with selective context hygiene targets a $9.0B = 3M teams × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 30% YoY — driven by rapid LLM/assistant adoption and enterprise AI budgets (source: blended estimates from Gartner and McKinsey on AI adoption and developer tooling spend).

Key trends driving demand: AI assistants are moving from pilots to everyday tools — this increases the volume of persistent context that must be managed and audited, creating demand for memory hygiene.; Enterprises are prioritizing AI reliability and explainability — tools that reduce hallucinations and create traceable context will be purchased to reduce risk.; Developers are adopting middleware and SDKs for LLM orchestration — a pluggable memory layer fits naturally between apps and models and speeds integration.; Vector stores and retrieval tooling are maturing, enabling more deterministic memory retrieval and selective forget mechanisms that products can leverage..