BulkheadOS is a company operating graph for complex software companies. It links websites, docs, support, sales, product behavior, procedures, tests, and AI guardrails so humans and agents work from the same governed truth.
What You Get
When you map a product area in BulkheadOS, you get:
- One governed product map - Connect public claims, docs, expected behavior, procedures, tests, and AI guardrails.
- Shared company language - Define the terms product, support, sales, documentation, and AI systems must use consistently.
- Traceable public surfaces - Serve pages, markdown alternates, schema, and AI-readable context from the same source of truth.
- Customer signal routing - Link support friction, sales objections, implementation gaps, and feedback back to product decisions.
- Verified action paths - Move from discovery into reviewed operator workflows without losing provenance or tenant isolation.
Who It's For
- Complex software companies - Teams whose websites, docs, support, sales, product decisions, and AI answers no longer line up.
- Product and knowledge operations teams - Teams turning scattered customer signal into governed product understanding.
- Support, CX, and sales engineering teams - Teams that need repeated friction and objections to become visible product inputs.
- AI platform teams - Teams trying to make agents accurate without asking them to infer the company from disconnected fragments.
Key Principles
- Link the company first - AI quality depends on explicit company structure, not larger prompts over scattered content.
- Governance is the product - Claims, docs, procedures, tests, and AI guardrails change as one reviewed system.
- Public and operational truth stay connected - The website, knowledge base, support reality, and product decisions share one graph.
- Discovery can become verified action - AI-readable surfaces point back to provenance-preserving operator workflows.
Show Me Real Examples
The best way to understand BulkheadOS is to see the surfaces in use. These pages show how the operating graph becomes documentation, search, feedback, and AI discovery.
Code Examples
Shiki, code blocks, and copy buttons
See how Markdown preview, syntax highlighting, and multi-language snippets work in practice.
Search
Orama-powered edge search
Inspect the exact request and response shape for the zero-D1 runtime search endpoint.
Feedback
Page feedback and editorial loops
Capture quick yes or no signals, then route them back into docs maintenance work.
Fediverse
Federated comments and reply threads
Show readers how comments flow back from ActivityPub into your site UI.
Next Steps
Start with the AI discovery guide, then use the Quickstart to understand how a tenant surface is published.