Testing & Simulation
Don't guess whether optimizations will work. RAG simulation and controlled experiments predict retrieval success before you deploy to production.
Why test before deploying?
AI visibility changes take weeks to propagate through crawl cycles. A failed optimization wastes months. Moderated testing validates changes in hours, not weeks — letting you iterate fast and fail cheap.
RAG simulation (DEPT-style)
We simulate how RAG pipelines process your content — from chunking through embedding to retrieval ranking:
Split your pages the same way RAG systems do (512-1024 tokens)
Create vector embeddings using common models (OpenAI, Cohere)
Embed target queries and measure distance to your chunks
Predict where your content ranks against competitors
Embedding distance audits
The closer your content embedding is to the query embedding, the more likely it gets retrieved:
Directional alignment between query and content vectors
Absolute distance in embedding space
Key concept coverage between query and content
Controlled experiments
Before rolling out changes site-wide, we test on isolated pages:
Test headline, structure, or depth changes
Measure impact of adding/modifying structured data
Test dateModified and lastmod effects
Optimize placement of extractable facts