Trusted by top startup founders
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Proprietary AI models trained on over 130 Million transactions totaling $705 Billion
Natural language processing
Digits uses state-of-the-art natural language processing (NLP) to understand banking transactions, invoices or contracts to assist users with their accounting tasks and questions.
Vector Similarity Model
Models trained to find similar transactions or vendors, eliminating the need to manually categorize every expense. This allows for pattern recognition and the ability mimic the unique behaviors of each individual Digits user.
AI Agents
Digits was one of the first companies leveraging AI Agents to deliver autonomous decision making for tedious accounting workflows.
Custom large language models
Custom large language models parse through transactions, bank statements, contracts, and invoices to give better context for classifications.
In-house Generative AI
Deployment of in-house, generative AI models capable of generating answers, graphs, and insights to financial prompts.
When we say AI Bookeeping, we actually mean it.
AI at the core
| Digits | Pilot | Zeni | Puzzle | Finta | |
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| Automated coding of transactions Automates tedious transaction classifications without AI. |
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| Data is secured and encrypted at rest Customer data is object-level encrypted and never exposed to 3rd parties. |
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| SOC2 Type II certified Externally validated organizational commitment to data security. |
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| In-house ML and AI team AI models built and deployed by a industry leading experts and best-selling subject matter authors. |
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| Generative AI Deployment of in-house generative AI models capable of generating answers, graphs, and insights to financial prompts. Data is never exposed to 3rd parties. |
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| Industry Relationships Ongoing collaboration and partnership with leading AI companies such as NVIDIA and Google. |
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| Similarity models Ability to mimic individual accountant decisions, no generic classification model. |
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| Custom-trained, deep-learning and large-language financial modeling engine We continuous improve of ML models based on customer feedback (not possible on GPT-4). |
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| Object-level encrypted data Models are trained with data that’s object-level encrypted throughout the entire process. |
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AI infrastructure
| Digits | Pilot | Zeni | Puzzle | Finta | |
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| Vector similarity search Similarity models trained to find similar transactions or vendors, eliminating the need to manually categorize every expense. |
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| Natural language processing Natural language processing (NLP) is used to understand banking transactions, invoices or contracts to assist users with their accounting tasks and questions. |
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| Classification models Machine learning classification models that predict categorical outcomes using groups of historical data. |
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| Self-critical agents AI agent computer programs that perceive and interpret its environment in order to autonomously perform actions and make decisions — automating tedious accounting tasks, such as identifying transactions for accruals / depreciation schedules and automatically creating entries and supporting documentation. |
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| Machine learning ops Machine learning operations that combines machine learning, data engineering, and devops to standardize and streamline ML model deployment and management. |
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Object-oriented financial modeling engine
| Digits | Pilot | Zeni | Puzzle | Finta | |
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| Object-oriented data layer High performing data representation to provide deeper understanding of financials and improve model training. |
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| Vendor database An extensive vendor dataset that standardizes our clients ledger while easing year-end tax preparations. |
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| Insight algorithms Constant variance analysis that tracks momentum and magnitude of charges in order to promptly flag what matters. |
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| Recurrence detection Deep learning that detects recurring transactions, even upon their first arrival. |
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| Proprietary layout-aware language models and datasets Machine learning models, ranging from deep learning to state-of-the-art, fine-tuned large language models. |
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| Reconciliation Real-time reconciliation, categorization, and classification of inbound transactions and financial data. |
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AI system architecture
| Digits | Pilot | Zeni | Puzzle | Finta | |
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| High scale Built to scale; the analysis and processing of millions of transactions daily. |
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| Search Comprehensive indexing that allows you to search anything and see where it shows up in your ledger. |
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| Speed to answers Real-time analysis engine providing the ability to return the full details for any transaction, vendor or invoice, as well as perform complex multi-dimensional aggregations to get the answers you need. |
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| Sharing Ability to share reports, vendor, transaction or category details in isolation with anyone. |
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| Document processing Layout language models trained to integrate visual (layout and format) information with textual context in order to extract information and convey meaning. |
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| Interactivity Large-language models trained to perform accounting-related tasks and allow will receive detailed, instant answers. |
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If companies aren’t building and deploying proprietary models in-house they’re just sending your data to ChatGPT.
Switch to Digits today
Experience accounting, reimagined.
