Byte Digital's knowledge base publishes connected, source-linked company information for people, AI agents and research tools. It uses static HTML articles, Markdown reading copies and a shared content source to make the same information available in several useful formats.
One business, two complementary reading experiences
The main Byte Digital website presents services, resources and routes to enquire. The wiki at bytedigital.io/knowledge/ provides a dedicated reference library. It is a section of the same website, with its own navigation and article structure.
The library is publicly readable. People and automated tools can access the same article content. The story behind the project explains why Byte launched it on 12 October 2026.
Articles organised around a question or subject
Business pages describe the company, founder and working arrangements. Service and sector guides explain what Byte does and the circumstances in which that experience is relevant. Project records give the context, delivery scope and published results of specific work.
Each article uses a descriptive heading, a short introduction, clearly labelled sections and contextual links. Results retain their reporting period and measurement definitions. Links back to the main website connect summaries with original case studies, services and practical guides.
HTML and Markdown from the same source
The editorial source is a collection of Markdown articles. A build process creates the HTML pages, individual Markdown reading copies, search index and complete text collection. Shared business facts feed those outputs from one record.
The article body appears before the shared knowledge-base navigation in the HTML document. This supports readable extraction without placing two copies of the entire menu ahead of the unique content. The layout includes an article contents list, topic navigation and a search facility for human readers.
Discovery files and structured data
| Resource | Purpose |
|---|---|
| XML sitemap | Lists the canonical article URLs and meaningful modification dates. |
| AI reading index | Provides a concise, linked guide to the collection. |
| Complete text | Makes the article collection available as one text resource. |
| Individual Markdown copies | Provide the corresponding article without the visual page layout. |
| Structured knowledge graph | Connects the business, founder, website and articles using Schema.org JSON-LD. |
Structured data describes visible content and the relationships between entities. Titles, descriptions, canonical links and article dates also appear in the page metadata. The reading files complement the HTML pages and ordinary internal links.
Google's published guidance says that its AI search features do not require special AI schema or a new machine-readable file. The llms.txt format is a community proposal: Byte provides it as an additional reading route, without assuming that every assistant uses it. These choices sit alongside the technical and content work described in AI SEO and GEO.
Evidence and editorial maintenance
Articles link to the relevant published sources. Project figures retain their scope; company information connects with the legal business identity; founder experience has its own timeline. The publication record preserves a dated evidence package for the wiki itself.
Every article displays its first publication, latest modification and latest review dates. Substantive content changes update the modification date. A review date records an actual editorial check. The change history records significant changes to the collection.
Testing access and usefulness
Byte's release checks cover page responses, links, canonical addresses, metadata, JSON-LD, the sitemap and agreement between published files and the prepared release. Browser checks assess the human reading experience. Crawler checks assess whether requests receive article content rather than an access challenge.
Testing an AI assistant adds a different question: can it retrieve the relevant information and use it accurately? Useful test prompts ask about Byte's NHS campaigns, local authority survey advertising, home-care Google Ads, ecommerce work and international Spotify experience. A test record should capture the assistant, date, prompt, cited URLs and any factual corrections needed.
Retrieval, search indexing, citation and recommendation are distinct outcomes. A successful page request establishes access for that request; a cited answer establishes that the particular answer used the source. Measurement and reporting explains Byte's wider approach to keeping measures meaningful.
Applying the approach to a business
A useful company reference library starts with real customer questions, accurate business information and evidence. Its value comes from clear content and maintenance as well as technical accessibility. Byte combines these disciplines through its AI search optimisation, SEO and website design work.
How the library connects with the wider service
Byte's company knowledge-base development service applies this approach to a client's information. It sits within a wider programme of AI visibility auditing, content and evidence development, technical improvements and measurement.
The main website explains what the client can commission. The wiki documents the method and relevant evidence. A change to the service prompts a review of both descriptions, so scope, working arrangements and source links stay aligned. AI SEO engagements records the commercial and delivery arrangements.
For a buyer's introduction, see the llms.txt and schema guide.
Development for another company
The knowledge-base development method explains client content inventories, interviews, article planning, implementation and maintenance. The technical access method records the checks used around public content. The Byte website case study brings the completed work together.