An AI platform needs to explain what it does, who it serves, and how someone can begin using it. Design connects those questions across the brand, website, technical content, and systems used to keep that information current.

For founders and product leaders, design for AI products includes the experience people encounter before they ever run a model or configure an agent. Your website, documentation, and visual language help people decide whether the platform fits their needs.

At Monogram, our work with Fireworks AI, CrewAI, and Contextual AI shows how those decisions extend from visual identity into content architecture and delivery.

Start with the decisions your audience needs to make

An enterprise buyer and an engineer arrive with different questions. A buyer may need to understand the platform’s business application; an engineer needs enough technical detail to evaluate how it fits into an existing system.

Before choosing layouts or interactions, define what each audience needs to learn:

Purpose: What problem does the platform address, and for whom?
Capabilities: What can someone build or accomplish with it?
Evidence: Which technical resources, examples, or research support its claims?
Next step: Should the visitor explore documentation, evaluate a use case, or speak with the team?

These questions give design a concrete responsibility. Navigation, diagrams, page structure, and calls to action should help visitors make those decisions.

Can someone understand where your platform fits without already knowing the terminology your team uses internally?

Build a visual system that teams can use

A visual identity needs rules that hold together as a company adds pages, campaigns, and partnerships. The deliverable should include enough structure for the people producing those materials to work consistently.

For Fireworks AI, we designed a new brand identity and web design foundation. The work included a logo system, raster grid, typography lockups, buttons, background textures, and Figma templates for social, digital, and print production.

The identity used a dark background to reflect the platform’s technical audience, with Fireworks Purple as its defining color. A construction grid gave the logo system rules for future variations and partner placements.

Fireworks AI’s engineering team built the marketing pages using our design foundation. That division of responsibility shaped what we delivered: reusable elements and website examples that could support additional pages with little further design work.

For your platform, the practical question is whether another team can apply the identity correctly. Reusable components, documented rules, and production templates make that possible.

Connect page design to content structure

A website needs to accommodate changes in messaging, research, and product capabilities. That requires a content model that supports the design.

In our work with CrewAI, the Figma component library mapped directly to structured models in Contentful. Reusable sections included platform diagrams, pricing, feature grids, tabbed content, and technical documentation.

This modular architecture gave marketing teams components for composing pages while engineering retained governance over the underlying implementation.

We also connected Ghost to Contentful through an automated publishing workflow. Published Ghost articles become drafts in Contentful, where marketing reviews, enriches, schedules, and publishes them.

That review boundary is deliberate. Automating the transfer streamlines production; publication remains an editorial decision.

When you design a content platform, define who creates information, who reviews it, and which parts of a page can change independently. Those responsibilities belong in the architecture as well as the interface.

Treat existing content as part of the design problem

A redesign needs to account for the material a company already publishes. Research articles, use cases, and technical explanations may need different structures, and older imagery may no longer fit the new visual system.

For Contextual AI, we combined a refreshed identity with a website built using Sanity, Next.js, and Vercel. The Sanity schemas supported migration from WordPress across blog posts, research articles, and use cases.

Automated scripts preserved content structure and metadata, alongside manual review and corrections. Where existing content did not fit the new layouts, we adapted its structure. We also created custom imagery aligned with the refreshed identity and page messaging, and added real-time content previews.

The lesson for an AI platform redesign is to assess content early. Establish which information must be preserved, which material needs restructuring, and how editors will review future changes. That work makes the new design usable beyond launch.

Make the explanation accessible to AI systems

People may encounter your platform through an AI assistant before visiting the website. Discoverability and crawlability therefore belong alongside the reading experience: important explanations need to be accessible, clearly structured, and available as text.

CrewAI’s website also supports Markdown content delivery and an MCP integration for retrieving public resources. These provide additional ways for software to consume the platform’s information.

Access alone does not establish citation performance. Google’s guidance for AI search features emphasizes established SEO practices, accessible textual content, and internal links; it does not require special AI markup or guarantee inclusion. Google Search Central

For an article, use case, or product page, explain the capability in self-contained language. Keep the claim close to its supporting evidence, and describe diagrams in accompanying text. This gives readers and systems a clearer source to interpret.

Plan for the next release

Before approving a design system, test it against an upcoming product announcement, a detailed research article, and a new use case. Ask the teams responsible to assemble those pages with the proposed components and content models.

That exercise exposes gaps while they are still inexpensive to address. It also gives you a practical acceptance criterion: your team can explain the next capability, publish the supporting material, and preserve the platform’s visual and technical consistency.