System inventory
Capture tokens, components, usage rules, code paths, and source-of-truth gaps without pretending the system is cleaner than it is.
AI design system readiness
Make your design system usable by AI agents.
Aktive helps product, design, and engineering teams turn scattered design-system knowledge into agent-readable rules, component mappings, review gates, and proof packets so AI-assisted UI work stays closer to the system.
$2,995/mo
One active request
Pause between requests
Best for
- B2B SaaS teams adopting AI coding or design tools.
- Design-system owners seeing AI-generated UI drift from the system.
- Product and engineering leads who need reviewable proof before AI-assisted work ships.
Common outcomes
- Agent-readable design-system rules.
- Component, token, and Figma-to-code mapping notes.
- Human review gates and proof packet evidence.
Source-linked proof
Public work before private claims.
conversion flow
Consumer conversion flow
Aktive can tighten a buyer-facing flow around the next action without reducing it to a generic landing page.
Inspect source
healthcare workflow
Healthcare product workflow
The work can make sensitive, high-context product workflows readable without leaning on AI hype visuals.
Inspect source
product ui
Product UI breadth
Aktive can handle high-information software surfaces while keeping hierarchy and review paths clear.
Inspect source
AI-era design operations
The problem is not more design-system documentation.
Most teams already have fragments: Figma files, component libraries, code conventions, docs, launch notes, and designer memory. AI agents need that knowledge translated into constraints they can follow and evidence humans can inspect.
Agent packet
Turn system knowledge into instructions a supervised AI coding or design agent can use before touching the interface.
Proof gates
Define the screenshots, tests, review notes, and evidence needed before AI-assisted product work is trusted.
Decision support
What to decide before booking.
Use this page to decide whether the first request is concrete enough for a focused monthly lane.
Strong first request
Audit one product surface and identify where agents would misuse the design system.
Strong first request
Create an agent-ready design-system packet for a real feature or workflow.
For AI coding adoption
Cursor, Codex, Claude Code, and similar tools are stronger when the design system is expressed as usable constraints, not just scattered docs.
Compared with design-system docs
Docs explain the system. A readiness packet tests whether agents can use it correctly and whether humans can verify the output.
Fit check
When this is not the right lane.
Aktive is intentionally narrow. Clear boundaries make the work faster, cleaner, and easier to review.
Teams that want a generic design-system docs site with no implementation proof.
Fully autonomous AI design governance without human review.
Companies with no existing product surface, component direction, or design language to audit.
Broad enterprise transformation before one useful proof packet exists.
Plain answer
Aktive offers an AI design system readiness sprint for teams that want humans and AI agents working from the same product, design, and engineering truth.
What is AI design system readiness?
It is the work of making a design system clear enough for AI-assisted tools to use and structured enough for humans to review. That means tokens, components, usage rules, code mappings, agent instructions, QA gates, and proof evidence.
What does Aktive produce?
A focused readiness packet: system inventory, token and component notes, Figma-to-code mapping gaps, agent-readable instructions, review gates, and a proof packet showing how the system should be used.
Does this replace Figma, Storybook, zeroheight, or Supernova?
No. Those tools can remain the source systems. Aktive helps translate scattered design-system knowledge into practical rules, packets, and review evidence for AI-assisted product work.
Can this help teams using Cursor, Codex, Claude Code, or Figma MCP?
Yes. The point is to reduce vague prompts and one-off UI guesses by giving agents clearer system context, allowed components, token rules, and human review criteria.
Choose by need
Use these routes as a decision path before booking the fit call.