Experience the magic of AI accounting today.
Annual Report 2025
Beyond the AI Hype
AGL outperforms LLMs by 54% for accounting tasks
Trusted by top startup founders
How AGL works
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Proprietary AI models trained on over 170 Million transactions totaling $825 Billion
AI Agents
Digits was one of the first companies leveraging AI Agents to deliver autonomous decision making for tedious accounting workflows.
Vector Similarity Models
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.
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.
Custom large language models
Custom large language models parse through transactions, bank statements, contracts, and invoices to give better context for classifications.
Dozens of models working in harmony
Deployment of in-house, generative AI models capable of generating answers, graphs, and insights to financial prompts.
First-class ML competency
Dedicated ML team AI models built and deployed by a industry leading experts and best-selling subject matter authors. |
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Exclusive Industry Relationships Ongoing collaboration and partnership with leading AI companies such as NVIDIA and Google. |
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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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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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Automated coding of transactions Automates tedious transaction classifications. |
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Proprietary AI orchestration
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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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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Built to scale Built to scale; the analysis and processing of millions of transactions daily. |
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Top features powered by AGL
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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