Studiograph
The intelligence layer between your design practice and AI.
Translate your operations, methods, and design craft into a structured knowledge graph that AI agents can act on.
AI output feels generic because it lacks your context.
ChatGPT and Cursor can generate functional interfaces and write passable proposals. But without context about your specific methods and standards, the output is interchangeable. It could have come from any team.
The knowledge that makes your work distinctive — operational methods, component behaviors, aesthetic point of view — lives in people's heads, scattered documents, and tribal memory. It's not accessible to AI.
Operational Knowledge
How you scope projects, write proposals, staff teams, manage clients, produce case studies — the methods that make your practice run.
Design Knowledge
Your component behaviors, interaction patterns, aesthetic instincts — the thousands of implicit rules that make your work yours and not someone else's.
When someone leaves, this knowledge walks out the door. A mediocre proposal loses a deal. A generic interface loses a user.
Two interconnected layers of organizational knowledge.
Studiograph captures everything a design team knows — both how you run the business and how you do the work — as a structured, interconnected graph that AI agents can act on.
Operational Graph
How your design team runs its business — encoded as structured, composable files organized into executable workflows.
- Prospect research & outreach
- Proposal generation
- Project scoping & staffing
- Client communication
- Case study production
- Awards submissions
“When agents generate a proposal, it reads like your proposals.”
Design Graph
The craft that defines your creative output — documented as enforceable design laws rather than suggestions.
- Component specifications & states
- Interaction patterns & behaviors
- Aesthetic philosophy & rationale
- Typography & color systems
- Animation & transition rules
- Accessibility standards
“When agents scaffold an interface, it follows your design system.”
Knowledge compounds over time
Every project you complete adds to the graph. Your fifth project inherits the design decisions from the first four. New team members onboard against the graph instead of relying on oral history. Your methods become durable instead of fragile.
Built for product design teams.
Design is where AI's context gap is most visible and most costly.
Independent Studios
2–15 people
Client-facing practices where scaling beyond the principals' capacity is the primary growth constraint.
Boutique Agencies
Digital product & UX strategy
Teams specializing in design systems where institutional knowledge walks out the door with every departing team member.
In-House Teams
Technology companies
Product design teams looking to systematize design operations and accelerate AI-assisted workflows without sacrificing quality.
The teams that figure out how to give AI their specific context will produce better work, faster, at a scale that was previously impossible.
Built on principles that last.
No vendor lock-in. No black boxes. Your knowledge stays yours.
Markdown-native
All knowledge stored as plain files with structured metadata — no vendor lock-in, readable for decades.
Version-controlled
Full history, branching, collaboration, and audit trails via Git.
Model-independent
Use the best AI model for each task — reasoning for architecture, writing for docs, code for implementation.
Local-first
Data stays on your team's own infrastructure. You own your knowledge graph.
Agent-orchestrated
Specialized AI roles — researcher, strategist, analyst — work collaboratively through structured workflows.
Human-in-the-loop
Agents augment judgment rather than replacing it — review and approval steps built into every workflow.
Design intelligence for AI-native teams.
Join the waitlist to be among the first to transform how your team works with AI.