Accounting, re-architected.

The first new accounting platform in 20+ years.

AI at the core

Vector similarity search

We use similarity models to find similar transactions or vendors, eliminating the need to manually categorize every expense. These models work by finding the correlation between two data points, and making predictions or assigning labels in related instances.

Classification models

Machine learning (ML) classification models predict categorical outcomes using groups of historical data. At Digits, we use these models to make decisions through a combination of statistical heuristics, classical machine learning or deep learning for things like determining parties and recurrences, identifying categories via account type (assets, liabilities, equity, etc.), and mapping transactions.

Machine learning operations

Machine learning operations (MLOps) combines machine learning, data engineering, and devops to standardize and streamline ML model deployment and management. Our engineers actively collaborate with Google and other companies to develop best practices in the MLOps space, with the goals of faster and more robust experimentation, reliable deployment, and better maintenance of ML models.

Self-critical agents

An AI agent computer program perceives and interprets its environment in order to autonomously perform actions and make decisions. AI Agents help Digits automate tedious accounting tasks, such as identifying transactions for accruals / depreciation schedules and automatically creating entries and supporting documentation.

Natural language processing

The objective of natural language processing (NLP) is to read, decipher, understand, and make sense of human language in a valuable way. We use NLP to understand banking transactions, invoices or contracts to assist users with their accounting tasks and questions. It helps that members of the Digits team co-authored one of the most popular introductions to NLP.

Object-oriented financial modeling engine

Core system architecture

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Security

All of this achieved while building security into every level.

Accounting, re-architected.

The first new accounting platform in 20+ years.

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AI at the core

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Vector similarity search

We use similarity models to find similar transactions or vendors, eliminating the need to manually categorize every expense. These models work by finding the correlation between two data points, and making predictions or assigning labels in related instances.

Classification models

Machine learning (ML) classification models predict categorical outcomes using groups of historical data. At Digits, we use these models to make decisions through a combination of statistical heuristics, classical machine learning or deep learning for things like determining parties and recurrences, identifying categories via account type (assets, liabilities, equity, etc.), and mapping transactions.

Machine learning operations

Machine learning operations (MLOps) combines machine learning, data engineering, and devops to standardize and streamline ML model deployment and management. Our engineers actively collaborate with Google and other companies to develop best practices in the MLOps space, with the goals of faster and more robust experimentation, reliable deployment, and better maintenance of ML models.

Self-critical agents

An AI agent computer program perceives and interprets its environment in order to autonomously perform actions and make decisions. AI Agents help Digits automate tedious accounting tasks, such as identifying transactions for accruals / depreciation schedules and automatically creating entries and supporting documentation.

Natural language processing

The objective of natural language processing (NLP) is to read, decipher, understand, and make sense of human language in a valuable way. We use NLP to understand banking transactions, invoices or contracts to assist users with their accounting tasks and questions. It helps that members of the Digits team co-authored one of the most popular introductions to NLP.

Object-oriented financial modeling engine

Core system architecture

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  • High scale

    Digits is built to scale; we analyze millions of transactions daily, and our team has experience processing trillions of events per day. We help you bring order to your finances today and into the future, as your business grows.

    Speed to answers

    AVS, our proprietary real-time analysis engine, puts your business’ finances at your fingertips. In milliseconds, you can return the full details for any transaction, vendor or invoice, as well as perform complex multi-dimensional aggregations to get the answers you need.

    Search

    Digits’ search functionality helps you find the needle in the haystack. Search your books with blazing fast results, returned as you type. Our comprehensive indexing means you can paste anything into search and see everywhere it shows up in your books.

  • Sharing

    Business finance is a collaborative effort among your team. Any vendor, transaction or category can be securely shared in isolation with anyone.

    Document processing

    Upload forms, invoices, or scanned documents. Our layout language models are trained to integrate visual (layout and format) information with textual context in order to extract information and convey meaning.

    Interactivity

    Digits has a brain that communicates and interacts like a human — and even performs accounting-related tasks. We fine-tuned hundreds of large language models, so that you can ask Digits questions and will receive detailed, instant answers.

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Security

All of this achieved while building security into every level.

Welcome to Digits

If you ever hoped that accounting could be better—let's talk.

Thank you!

We're working through the waitlist as quickly as possible.

We’ll be in touch soon - stay tuned!

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