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Tight’s AI-first architecture has the benefit of learning from the behaviors of the 1.3M+ SMBs and 3k+ bookkeepers that have used Tight to categorize their transactions. Given the importance of seeing real bookkeeper categorization behavior, Tight has the distinct advantage of being able to combine the latest in AI/learning technology with (anonymized) data from the 1.3M+ SMBs in the Tight database, all already segmented by business type so that all learnings are properly contextualized.

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New Transaction Ingestion Flow

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Rule Insertion Flow

As Tight adjusts its heuristics to leverage the latest in rapidly evolving AI technologies, it reruns the user/bookkeeper actions taken upon the transactions of 1.3M SMBs in order to retrain the Tight Rules Engine: The flows above give a high-level overview into how Tight is able to leverage its history to provide the best categorization outcomes. These outcomes get smarter every day.

The specific types of rules that Tight creates are proprietary, evolve regularly, and have been purposefully excluded from the above diagrams.
Any given transaction can be audited via the API and Embeddable UI so that your team or your user can understand why a given transaction was categorized a specific way.