ContextOS

Every engineering org in 2026 has adopted AI coding tools, but the tools are amnesiac and solitary: each session starts cold, each tool keeps private context, and the institutional knowledge AI needs to be correct lives in human heads and stale wikis.

ContextOS is the context layer and control plane that sits around the AI tools teams already use — persistent shared memory, codebase intelligence, an MCP integration hub, and governance + observability, all in one place.

Teams have superhuman code-generation capacity but subhuman code-understanding continuity. AI repeats known mistakes, new hires and agents ramp slowly, AI spend is invisible and unbounded, and nobody can govern or audit what the AI actually did.

The model is a rented commodity and the editor is a crowded battlefield. The durable, defensible asset is the team's accumulated context and the controls around it — which IDE and model vendors are structurally disincentivized to make portable.

A multi-tenant SaaS plus a thin local layer (CLI + MCP server) organized as five planes: a memory plane (versioned institutional knowledge), a knowledge plane (codebase understanding, reuses #2), an integration plane (MCP hub, reuses #3), an orchestration plane (context-loaded AI chat/workflows/agents), and a control plane (RBAC, audit, policy, billing).

The flagship workflow is the Context Handoff: corrections a developer makes in one tool are captured, reviewed, and stored as durable context, so the next session — any tool, teammate, or agent — starts warm and never re-makes the corrected mistake.