From CLI to context infrastructure.
We're building in three phases. Each one expands where knowledge is captured, how it's delivered, and who can use it. The organizations that start now get a compounding advantage.
The strategy
Most AI tools bolt intelligence onto existing workflows and hope for the best. The results speak for themselves — 75% of AI agent projects fail, and 44% of Copilot users stop because they don't trust the answers.
The problem isn't the AI. It's the context. If an AI tool knows nothing about your organization — your decisions, your processes, your architecture, your policies — it can only produce generic output. No amount of prompt engineering fixes that.
Our approach is different. Build the context layer first. Start with the people closest to the knowledge (developers and power users), give them tools to capture it, then progressively expand to every tool and every team in the organization. By the time you need AI agents that actually understand your business, the knowledge graph is already there.
Phase 1: Developer Tools & MCP
The foundation. A CLI and MCP server that lets developers and power users capture institutional knowledge, search it instantly, and deliver it to any AI tool.
Start here. Every piece of knowledge you capture now compounds — it makes every future AI interaction across your organization smarter.
Phase 2: Work Tool Integrations
Knowledge doesn't just live in code editors. Phase 2 embeds Apart Intelligence into the tools where your team already works — email, documents, spreadsheets, chat, and presentations.
This is where knowledge capture stops being a conscious act. Your team's existing workflow becomes the input. Decisions made in docs, processes discussed in Slack, policies shared over email — all captured automatically.
Phase 3: Context API for Agents
A public, stable API purpose-built for agentic workflows. Any AI agent — internal or third-party — can securely draw organizational context from your knowledge graph with fine-grained access control.
This is the endgame. When every AI agent your organization uses — whether it's a custom internal tool or a third-party SaaS — can securely access your institutional knowledge, the environment problem is solved. Context becomes infrastructure.
Where Phase 2 is headed
Knowledge capture should happen where your team already works. Phase 2 embeds Apart Intelligence into the tools you use every day.
The endgame: context as infrastructure
Phase 3 opens your knowledge graph to any AI agent — internal tools, third-party SaaS, custom workflows — through a secure, stable API. Every agent gets the context it needs, scoped to exactly what it should see.
Fine-grained access control means your sales agent sees client history but not engineering architecture. Your code review agent sees architecture but not HR policies. Context delivery with guardrails.
Scoped API keys
Per-agent keys with workspace, domain, and type restrictions
Audit trails
Every context request logged — what was accessed, by which agent, when
Token-aware delivery
Context budgets that respect each agent's context window limits
Team collaboration
Shared knowledge graphs with role-based access across your organization
The best time to start is now.
Every piece of knowledge you capture today makes every AI interaction tomorrow better. Join the beta and start building your organization's context advantage.