Track automatically pulls in all expense-type data from a variety of systems that you connect with it, including direct feeds from banks and credit cards, accounting systems, expense management systems, etc. Our algorithms then look for and identify known patterns within expense descriptions, memos, manual notes, and invoice metadata, and attribute these transactions to products that you use.

In many cases, our algorithms also identify expenses that have a high probability of being matched with a product and these are floated up to you for confirmation. Track uses both supervised and unsupervised machine learning making it adaptive in the sense that every new transaction coming into the system makes pattern-matching smarter and more efficient.

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