Zovda
Every deployed model holds a snapshot of facts, and every business changes facts faster than it retrains. A price, a policy, an address, a product that has been discontinued.
The model answers confidently with the snapshot, and the support team spends the week correcting it.
The fact and its validity window
A fact is not a string. It is a value with a start date, an end date, and a source of truth. Stored as a string, it can only be replaced; stored with a window, it can be reasoned about.
That difference decides whether a model can answer "what did this cost in March" at all.
Separate the knowledge from the style
Most training sets mix what to say with how to say it. When a fact changes, the whole example has to be rewritten, and the rewrite drags other things along with it.
Keep the answer skeleton and the fact slots apart. Then a price change touches one field, not a corpus.
Teach the update, not only the new value
A model that learns the new price has learned one string. A model that has seen fifty updates learns that facts expire, and starts hedging when its own knowledge is stale.
Include the correction pairs: old value, new value, date, and what the user should be told.
Measure staleness, not accuracy
Track, per fact category, how old the model's knowledge is at the moment of answering. A category that is two quarters stale needs a pipeline, not a bigger run.
Zovda builds these collections through Datuva, where each fact keeps its validity window. The data research collection shows how dated values are carried through a series.
Tell us which facts move fastest in your business. We reply within one business day.