Release Material: Investment / Partnership Version (Business Stakeholders)

Problem

Most "AI memory" offerings are feature-level add-ons. They lack auditable provenance, operational controls, and policy execution capability needed for reliable enterprise adoption.

Architecture Principles

1. Audit-first: verifiable source-of-record memory with commit lineage.
2. Derived async: resilient operations where derived pipelines do not interrupt core write availability.
3. Memory -> Policy: memory becomes an executable control layer for agent behavior.

Evidence

1. Multi-artifact distribution is established: GitHub, Docker, npm, PyPI.
2. Operational process exists: runbook, dead-letter handling, replay, consistency and health gates.
3. Technical proof can be reproduced with documented checks and release workflows.

Boundaries

1. Positioning is infrastructure-layer memory kernel, not a promise of universal model accuracy gains.
2. Industry benchmark scores are support signals; production KPIs remain the primary success criteria.
3. Enterprise-scale rollout depends on customer-specific security and tenancy constraints.

Next Step

1. Standardize release evidence packs for every market-facing update.
2. Package solution narratives by segment: personal builder, product team, enterprise partner.
3. Establish quarterly proof milestones tied to deployment scale and operational reliability metrics.