May 22, 2025·8 min
How to Build a Website for an AI Use-Case Knowledge Center
Learn how to plan, design, and launch a website that organizes AI use cases with clear structure, strong search, and governance for growth.
Learn how to plan, design, and launch a website that organizes AI use cases with clear structure, strong search, and governance for growth.
Before you design pages or choose a CMS, get clear on two things: who the knowledge center is for, and what you want it to achieve. This prevents building a “nice library” that nobody uses—and helps you make smart tradeoffs later (what to publish first, how deep each article should go, and what navigation matters most).
Most AI use‑case knowledge centers end up serving multiple groups, but one group should be primary. Common audiences include:
Write a one-sentence promise for each audience. Example: “For operations managers, we explain how AI reduces cycle time with real workflows and measurable outcomes.”
Decide what “good” looks like. Typical outcomes are:
If you’re aiming for evaluation support, you’ll likely need more detail per use case. If you’re aiming for inspiration, short, skimmable overviews may win.
A “use case” can be organized by industry (healthcare), function (finance), or workflow (invoice processing). Pick a primary meaning so content stays consistent.
A practical template is: problem → workflow → AI approach → inputs/outputs → value → constraints. This keeps articles comparable.
Choose a small set of measurable signals:
With goals, audiences, and metrics written down, every later decision becomes easier—and easier to defend.
A knowledge center works when visitors can predict where things live. Before you design pages, decide on the “shape” of the site: the main navigation, the core page types, and the shortest paths to the most common tasks.
For an AI use-case knowledge center, a simple top navigation often beats a clever one. A solid default is:
Keep it stable. Visitors will tolerate a lot, but not a menu that changes meaning across pages.
Use a small set of repeatable page types so the site stays consistent as it grows:
The goal is to reduce decision fatigue: visitors should recognize the page type within seconds.
Test your structure against real first clicks:
If these paths take more than 2–3 clicks, simplify the menu or add better cross-links.
Draw clear boundaries:
This separation keeps your use-case library clean and makes maintenance easier as content scales.
A knowledge center only scales when every use case is described the same way. A repeatable content model gives contributors a clear template, makes pages easier to scan, and ensures your filters and search can rely on consistent fields.
Define a small set of fields that must exist on every use-case page. Keep them plain-language and outcome-oriented:
If a page can’t fill these fields, it’s usually not ready to publish—and that’s useful signal.
Next, add structured metadata that supports filtering and cross-team discovery. Common fields include:
Make these fields controlled (picklists), not free text, so “Customer Support” doesn’t become “Support” and “CS.”
Non-technical readers want to know when not to use something. Add dedicated trust sections:
Implement the model as a page template (or CMS content type) with consistent headings and field labels. A good test: if you put three use cases side by side, users should be able to compare Inputs/Outputs/Value in seconds.
A good taxonomy lets readers find relevant use cases quickly—without needing to understand your internal org chart or technical jargon. Aim for a small set of predictable labels that work across industries and job roles.
Use categories for the few “big buckets” that define the primary purpose of a use case (e.g., Customer Support, Sales, Operations). Keep category names simple and mutually exclusive where possible.
Add tags for secondary attributes people commonly browse by, such as:
Finally, turn the most important tags into filters in the UI. Not every tag needs to be a filter—too many options creates decision fatigue.
Taxonomies fail when anyone can invent new tags freely. Define lightweight governance:
Beyond category and tag pages, design collection pages that group use cases by theme, such as “Quick wins with existing data” or “Automation for compliance teams.” These pages provide context, curated ordering, and a clear starting point for newcomers.
Each use case should include purposeful cross-links:
Done well, taxonomy and cross-linking turn a library into an experience readers can navigate confidently.
If your knowledge center has more than a handful of AI use cases, navigation menus won’t scale. Search and filtering become the primary “table of contents,” especially for visitors who don’t know the right terminology yet.
Start with full-text search, but don’t stop there. Non-technical readers often search in outcomes (“reduce churn”) while your content might be written in methods (“propensity modeling”). Plan for:
Decide early whether results should prioritize titles, short summaries, or tag matches. For a use-case library, title + summary relevance usually beats deep body matches.
Faceted filters help people narrow down quickly. Keep facets consistent across the library and avoid too many options per facet.
Common facets for AI use cases include:
Design the UI so users can combine facets and still understand “where they are” (e.g., showing selected filters as removable chips).
Zero results shouldn’t be a dead end. Define behavior such as:
Treat search analytics as your content backlog. Track:
Review this regularly to add synonyms, improve titles/summaries, and prioritize new use cases people are actively seeking.
A knowledge center only works if someone who’s curious (not expert) can understand what they’re looking at within seconds. Design every page to answer three questions quickly: “What is this?”, “Is it relevant to me?”, and “What can I do next?”
Use a repeatable layout so readers don’t have to re-learn the interface on each click.
Hub pages (category pages) should be scan-friendly:
Detail pages (one use case) should follow a simple pattern:
Summary (plain-language outcome)
Who it’s for (roles + prerequisites)
How it works (steps)
Example (prompt, workflow, or short demo)
What to try next (related use cases + CTA)
Keep CTAs helpful and low-pressure, such as “Download the template,” “Try the sample prompt,” or “See related use cases.”
Non-technical readers get lost when the same idea is called three different things (“agent,” “assistant,” “workflow”). Pick one term, define it once, and reuse it everywhere.
If you must use specialized terms, add a lightweight glossary and link to it contextually (for example: /glossary). A short “Definitions” callout on detail pages also helps.
Whenever possible, include one concrete example per use case:
Examples reduce ambiguity and build confidence.
Design for readability and navigation:
Accessibility improvements usually make the experience better for everyone, not just a subset of users.
Your CMS shouldn’t be chosen for popularity—it should be chosen for how well it supports publishing and maintaining use cases over time. An AI use-case knowledge center is closer to a library than a marketing site: lots of structured pages, frequent updates, and multiple contributors.
Look for a CMS that handles structured content cleanly. At minimum, you’ll want:
If these are hard to implement or feel “bolted on,” you’ll pay for it later in messy content and inconsistent pages.
A traditional CMS with a theme is usually faster to ship and easier for small teams to manage.
A headless CMS + frontend can be a better fit when you need a highly customized browsing experience, advanced filtering, or want the knowledge center to share content with other surfaces (like a docs portal). The tradeoff is more setup and ongoing developer involvement.
If you want to move even faster—especially for an internal-first or MVP knowledge center—tools like Koder.ai can help you prototype the core experience (React frontend, Go backend, PostgreSQL) via a chat-driven workflow, then iterate on taxonomy, filters, and templates with snapshots and rollback as you learn what readers actually use.
Even a “learning-first” knowledge center needs a few connections:
Set up clear stages (and match them to environments): Draft → Review → Publish → Update. This keeps quality high and makes updates routine—especially important when use cases evolve with new models, data sources, or compliance guidance.
A knowledge center stays useful only if someone is clearly responsible for what gets published, how it’s reviewed, and when it’s refreshed. Governance doesn’t need to be heavy—but it must be explicit.
Write a one-page style guide that every contributor can follow. Keep it practical:
Put the template in your CMS and make it the default for new use cases.
Even for a non-technical audience, AI use cases often touch sensitive topics. A lightweight review chain prevents rework and risk:
Use a clear “approve / request changes” step so drafts don’t stall in comments.
Assign an owner per page (a role or team, not a single person if possible). Define refresh rules such as:
When a use case is outdated, don’t delete it. Instead:
This preserves SEO value and prevents users from hitting dead ends when old links circulate in docs, emails, and support tickets.
SEO for a knowledge center is mostly about consistency. When every use case follows the same template and URL pattern, search engines (and readers) understand your library faster.
Define “defaults” once, then reuse them everywhere:
BreadcrumbList; optionally Article for blog posts and detailed guides). This improves clarity in search resultsPlan links like a curriculum:
Use descriptive anchor text (“fraud detection in claims” beats “click here”).
Use predictable URL patterns, for example:
/use-cases/<category>/<use-case-slug>//industries/<industry>/ (if you publish industry collections)Add breadcrumbs that mirror your structure so users can move up a level without using search.
Generate an XML sitemap that includes only indexable pages. Set canonical URLs for pages with variants (filters, tracking parameters). Keep drafts and staging pages noindex, and only switch to indexable when content is approved and internally linked.
A knowledge center works best when it teaches first and sells second. The trick is to define what conversion means for your organization—and then offer it as the next logical step, not a detour.
Not every reader is ready for a sales call. Pick 2–4 primary actions and map them to where users are in their journey:
Put calls-to-action after a reader has received value:
Keep the CTA copy specific: “See a demo for document classification” beats “Request a demo.”
Lightweight trust elements reduce anxiety while keeping the educational tone:
If you use forms, ask for the minimum (name, work email, one optional field). Offer an alternative like “Ask a question” that opens a simple form or directs to /contact—so curious readers can engage without committing to a full demo.
A knowledge center is never finished. The best ones get steadily easier to browse, search, and trust because the team treats the site like a product: measure what people try to do, learn where they get stuck, and ship small improvements.
Start with a lightweight analytics plan that focuses on intent and friction, not vanity metrics.
Set up analytics events for:
This event layer is what lets you answer practical questions like: “Are users finding use cases via navigation or search?” and “Do personas behave differently?”
Create a small set of dashboards that map to decisions:
Include leading indicators (search exits, time to first click, filter-to-view rate) alongside outcomes (newsletter signups, contact requests) so you can see both learning success and business impact.
Before launch—and after major navigation or taxonomy changes—run usability tests with 5–8 target users. Give them realistic tasks (“Find a use case that reduces support ticket volume” or “Compare two similar solutions”) and watch where they hesitate. The goal is to catch confusing labels, missing filters, and unclear page structure early.
Add a simple feedback loop on each page:
Review feedback weekly, tag it (missing content, unclear explanation, outdated example), and fold it into your content backlog. Continuous improvement is mostly disciplined triage.
A knowledge center will evolve over time, but the first launch sets expectations. Aim for a launch that feels complete to a first-time visitor: enough breadth to explore, enough depth to trust it, and enough polish to use it on any device.
Before you announce anything, run a practical checklist:
For launch, prioritize quality over volume. Pick 15–30 use cases that represent your most common buyer questions and the highest-value applications. A strong starter set usually includes:
Make sure each page has a consistent structure and a clear “next step” (e.g., related use cases, a demo request, or a template download).
Don’t rely on search on day one. Add entry points from:
If you build in public, consider incentivizing contributions. For example, Koder.ai offers an earn-credits program for creating content and a referral program via referral links—mechanisms that can also inspire your own knowledge-center community motions.
Set a recurring plan to avoid random additions. Each quarter, choose a focus such as:
Treat your roadmap as a promise to users: more clarity, better discovery, and more practical guidance over time.
Start by writing:
These decisions prevent a “nice library” that doesn’t get used and make later tradeoffs (depth, navigation, publishing order) much easier.
Pick one primary audience (even if you serve others) so the site has a clear default voice, depth, and navigation.
A practical approach is to write a one-sentence promise for each audience, then design the content and CTAs around the primary promise first.
A simple, predictable top navigation usually wins:
Use a small set of repeatable page types:
Repeatable types make the site easier to scan and easier to maintain as it grows.
Use a consistent template such as:
At minimum, ensure every page includes plain-language fields for Problem, Solution, Inputs, Outputs, Value, and Example. If you can’t fill these, the use case is usually not ready to publish.
Add dedicated sections that make limitations explicit:
These fields help non-technical readers understand when to use a use case and reduce overpromising.
Start with a few mutually understandable categories (big buckets like Support, Sales, Operations), then add tags for secondary attributes (industry, data type, outcome, maturity).
To prevent taxonomy sprawl, restrict tag creation to an editor group, define naming conventions, and merge duplicates with redirects when needed.
Make search forgiving and aligned with user intent:
For ranking, prioritize title + short summary matches (often more useful than deep body matches in a use-case library).
Treat it like a product moment, not an error state:
/contactAlso track zero-result queries—they’re a direct backlog for new content and synonym improvements.
Choose a CMS that supports structured, repeatable content and governance:
A traditional CMS ships faster for small teams; headless is better when you need highly custom discovery and advanced filtering—at the cost of more ongoing developer involvement.
Keep labels stable across the site so visitors can predict where content lives.