presence: sync card

README.md

@@ -26,15 +26,15 @@ short_description: We are Authentrics.ai
 
 Training is one of the most expensive and consequential parts of building a model. Once a run is
 complete, regressions, memorization, interference, or unwanted behavior can leave teams with a
-familiar answer: change the training setup and run it again.
+familiar answer: re-curate the dataset, change the training setup, and run it again.
 
 Authentrics provides another path — analyze how the model changed, trace where problems emerged,
 and correct the resulting model without a full retrain.
 
 ## What You Can Do With Authentrics
 
-- **Checkpoint-to-checkpoint comparison.** Parameter and activation drift measured between the checkpoints
-  a training run already saves — no extra instrumentation.
+- **Understand your model.** Establish a baseline across model behavior and internal structure,
+  detect meaningful changes, and localize which layers are most associated with them.
 - **Remove a known cause (machine unlearning).** When unwanted behavior traces back to specific training
   session(s), remove that effect directly from the checkpoint.
 - **Model optimization.** Recover accuracy, correct a regression, or suppress unwanted behavior by