Entity resolution and AI emulation
I emulate how each AI engine actually parses your @graph, find where entities fail to resolve, and fix the schema so resolution happens deterministically.
What it actually is
RankLabs runs your live or staging URLs through emulators for the major AI engines. Each emulator approximates how that engine extracts and resolves entities (different engines weight different fields, follow sameAs differently, and disambiguate Organization vs Brand differently). I find the cases where your @graph resolves under one engine and fragments under another, then adjust the schema until resolution holds across all of them.
This is the work no marketing-tier vendor can do, because it requires writing the emulator. Validators tell you the schema is well-formed. The emulator tells you that ChatGPT will merge your products under one brand entity while Gemini will split them. That delta is the whole game.
Deliverables
- Per-engine resolution report for every priority entity
- Diff log showing schema changes made to fix resolution failures
- Disambiguation test suite (added to the validator suite, runs in CI)
- Notes on engine-specific quirks the team should know about
- Resolution baseline captured before launch for post-launch comparison
What breaks without it
An entity that fails to resolve gets dropped from the engine's knowledge graph. You don't get cited. You don't get linked. You don't appear in answers about your category. And because each engine fails silently, you can't tell from the outside which engine sees you correctly and which sees a fragmented mess.
A common failure: a brand emits Product schema where each product references the Organization by nested object instead of @id reference. Google merges. Perplexity merges. ChatGPT splits, and treats every product as belonging to a different organization with the same name. Citations across the catalog collapse to zero on ChatGPT. Fixed with one architectural change to use @id references everywhere.
How it fits the Sprint
Emulation is the verification layer between architecture and ship. The architecture says how the graph should resolve. Per-template implementation builds it. Emulation confirms it actually does, before we declare the Sprint done and run the post-measurement scan.
The full Sprint breakdown
- 01componentFull @graph architectureOpen
- 02componentPer-template JSON-LDOpen
- 03currentEntity resolution and AI emulationyou are here
- 04componentValidator suite in CIOpen
- 05componentEngineer pair sessionsOpen
- 06componentPre/post citation measurementOpen
Stop pouring budget into a broken foundation.
If your SEO retainer hasn’t compounded, your AI citations have stalled, or your last technical audit ended in a deck nobody read, that’s not a content problem. It’s an engineering problem. The same engineer who diagnoses ships the fix.