How long until AI catches up with the truth?
When your facts change, the models don’t change with them. They keep repeating the old answer for weeks — sometimes months. Entidex measures exactly how far behind each engine is running, in days.
You rebrand on Monday. On Friday ChatGPT, Claude, Gemini, Perplexity and Grokare still using the old name. That gap isn’t random — it’s the time each engine takes to notice the record changed and re-ingest it. Most tools can tell you AI is wrong today. Information Lag tells you how long it has been wrong, and how much longer it is likely to stay wrong.
Three reads on the same clock
Engines stale
How many verified claims each engine is currently behind on — and the maximum open lag, in days, across everything it gets wrong.
Truth ahead
Where the record has already updated and no engine has caught up yet — the earliest warning that a stale answer is about to spread.
Surfaces diverging
Whether the AI engines are converging on the truth or drifting away from your other surfaces — and whether that gap is widening.
It works at two grains. Claim-level lag times how long a specific fact stayed stale before an engine corrected it. Field-and-engine lag shows which engine is furthest behind on which field right now. Both are reconciled through the canonical authority ladder, so a genuine lag is never confused with a wording difference — and all of it is a zero-cost aggregation over signals Entidex already holds.
Why the lag is the opportunity
The window between “the truth changed” and “AI caught up” is where reputational damage happens — a customer reads the old price, an investor reads the old ownership, a journalist quotes the old figure. It is also where you have the most leverage: publish the corrected record early and you shorten the lag for every engine at once. Pair Information Lag with LLM Knowledge Accuracy to see what is wrong, and the Entity Knowledge Statement to fix it.
Proof the correction landed
Because every observation is timestamped, Information Lag doesn’t just tell you an engine is behind — it confirms the moment it catches up. You get evidence that a fix worked, per engine, not a hope that it did.
Go deeper with Entidex
The full entity intelligence platform — beyond Explore Entidex
- Continuous multi-source entity observation
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Frequently asked questions
What is AI Information Lag?
It is the gap, measured in days, between when a fact about your entity becomes true and when the AI engines start repeating the new version. When you rebrand, move, launch, get acquired or change leadership, engines keep asserting the old answer until they re-ingest a corrected record. Entidex times that gap per engine and per field, so "AI is out of date about us" becomes a number you can watch shrink.
How is the lag measured?
Entidex holds a verified, timestamped record and compares it against each engine's claims over time. It logs the moment a field's ground truth changed and the moment each engine's answer flipped to match — the difference is the lag. It works at two levels: per claim (how long a specific fact stayed stale) and per field-and-engine (which engine is furthest behind on what).
What is "truth running ahead of the models"?
Sometimes the verified record updates and no engine has caught up yet — the truth is ahead of every model. That is the highest-value early state: you know exactly what AI is about to get asked and get wrong. Entidex flags it as an open positive lag so you can publish the corrected record before the stale answer spreads. It also surfaces the inverse — where an engine still holds a version the record has already superseded (ChatGPT, Claude, Gemini, Perplexity and Grok rarely move in lock-step).
Does this cost model credits to run?
No. Information Lag is a zero-cost aggregation over signals Entidex already stores — claim-truth transitions, per-field evaluation history, cross-surface divergence and drift. It reconciles them through the canonical authority ladder so a genuine divergence is separated from a mere selector artefact, then reports the open lag. No model is asked to grade anything.
How do I shorten the lag?
Publish a machine-readable verified record (the Entity Knowledge Statement) that engines with live retrieval can ingest quickly, and strengthen the authoritative sources behind the changed fact. Engines that retrieve live can update within days; those leaning on training data move on their own cycle. Information Lag tracks each engine so you can prove the correction actually landed rather than assuming it did.