Treat your content with the same rigor you treat your code. Version it. Govern it. Verify it upstream.
Software teams learned this decades ago: code that survives is code that gets tested, versioned, and maintained against a single source of truth. The practices that work—version control, continuous integration, automated testing—all exist to catch drift before it compounds.
Content follows the same logic. Both are structured information. Both require verification to have value. Both drift without governance. The difference is cultural, not fundamental.
When you treat content like code, you gain the same advantages: single sources of truth, upstream verification, governed generation, compounding returns on every verification you perform.
Verify once, generate many—the cost of verification amortizes across all outputs
Single source of truth—when the definition changes, everything updates
Upstream filtering—catch errors at the foundation, not in every document
Governed generation—AI produces content constrained by verified truths
Content-as-code isn't just better practice—it's measurably more efficient.
AI can generate content 150× cheaper than humans can verify it. The bottleneck isn't creation—it's verification.
Verify a canonical claim once. Generate unlimited outputs from it. The verification cost amortizes to near-zero.
Same knowledge base, three editions—Family, Business, Academic. Same truths, different presentations.
Four layers. Each with a distinct function. Together, they let you scale content generation without sacrificing accuracy.
Load-bearing truths verified by human judgment. These cannot change without explicit approval. Change one, and you're changing what the organization believes to be true.
Supporting material—definitions, examples, evidence, context. Referenced by canonical claims, reusable across all outputs.
Rendering rules that define how to present the same truths to different readers. Tone, depth, emphasis—all configurable without changing what's true.
AI-assisted assembly from verified components. Governed by configs, checked against canonical constraints, with drift detection built in.
Claims verified at the foundation, not after every output. Check once, trust everywhere.
When a definition changes, every output updates. No hunting for inconsistencies.
Generation constrained by layers 1-3. Aligned by design, not by review.
Accessible • Warm • Practice-focused
Strategic • Concise • Institution-focused
Rigorous • Cited • Methodology-focused
↑ Same 22 canonical claims ↑
We built The Book of Fire using this architecture. Three editions rendered from a single canonical knowledge base.
Same 22 load-bearing claims across all versions. Same definitions, same core evidence. Different tone, depth, and emphasis for different readers. The Family edition runs 45,000 words. The Business edition runs 20,000. The Academic edition includes full citations at 60,000.
The book demonstrates what we build for clients. One verification investment. Multiple outputs. Verification that scales.
Whether you're managing knowledge for multiple audiences, scaling AI-generated content, or establishing what your organization can trust—let's talk.