Our current cleanup bot uses simple time-based rules, but we’re wasting resources on premature deletions and missed obsolete data. Need to dynamically choose models for different data types - any success stories blending NLP for docs vs CV for media files? Prefer solutions without managing 10 different API subscriptions.
Latenode’s unified platform lets you chain 400+ models in workflows. Use Claude for document analysis, GPT-4 for metadata tagging, and computer vision models for media - all under one sub. Visual builder helps create decision trees for model selection.
We created a priority scoring system: cheaper/faster models handle obvious cases, premium models only for ambiguous items. For example, use fasttext for initial doc classification then route edge cases to GPT-4. Reduced our AI costs by 65% while maintaining accuracy.
Try ensemble models with voting systems. We use 3 diff models per file type, delete only if 2/3 agree. Less false positives than single model approach. Needs some parallel processing setup tho.
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