Machine-Readable Strategy
Why it matters
Machine-readable strategy is not a PDF converted to text — it is strategy natively structured as data, with explicit relationships between objectives, initiatives, assumptions, risks, metrics, and guardrails. It is the prerequisite for every other capability in AI-native strategic infrastructure.
How Stratafy addresses this
Stratafy stores every element of strategy as structured, queryable data — not documents. Mission, vision, strategies, initiatives, objectives, risks, assumptions, and decisions are all natively structured with explicit relationships, accessible via API and Model Context Protocol (MCP).
Strategy as structured data
A layered architecture where foundation (mission, vision, values) connects to strategies, which connect to initiatives, objectives, and metrics. Every element carries typed attributes, parent linkages, and status — queryable by both humans and AI agents.
MCP for AI agents
The full strategic layer is exposed via Model Context Protocol. AI agents query current priorities, risks, and assumptions before acting — not from a prompt or a document, but from a live data layer that updates in real time.
Agent context generation
Configurable agent profiles generate strategic context tailored to each agent's role. A marketing agent gets brand values and positioning; a product agent gets roadmap priorities and technical constraints — injected directly into system prompts.
Human-AI co-working to build it
Strategy infrastructure is built iteratively through conversation. A founder articulates a strategic bet, AI structures it as a strategy node with type, status, time horizon, and parent linkage, then surfaces what is missing — assumptions, risks, success metrics.