Dedicated ML team

AI models built and deployed by a industry leading experts and best-selling subject matter authors.

Exclusive Industry Relationships

Ongoing collaboration and partnership with leading AI companies such as NVIDIA and Google.

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.

Similarity models

Ability to mimic individual accountant decisions, no generic classification model.

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).

Automated coding of transactions

Automates tedious transaction classifications.

Vector similarity search

Similarity models trained to find similar transactions or vendors, eliminating the need to manually categorize every expense.

Natural language processing (NLP) is used to understand banking transactions, invoices or contracts to assist users with their accounting tasks and questions.

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.

Machine learning Ops

Machine learning operations that combines machine learning, data engineering, and devops to standardize and streamline ML model deployment and management.

Built to scale

Built to scale; the analysis and processing of millions of transactions daily.

Object-oriented data layer

High performing data representation to provide deeper understanding of financials and improve model training.

Vendor database

An extensive vendor dataset that standardizes our clients ledger while easing year-end tax preparations.

Insight algorithms

Constant variance analysis that tracks momentum and magnitude of charges in order to promptly flag what matters.

Recurrence detection

Deep learning that detects recurring transactions, even upon their first arrival.

Proprietary layout-aware language models and datasets

Machine learning models, ranging from deep learning to state-of-the-art, fine-tuned large language models.

Reconciliation

Real-time reconciliation, categorization, and classification of inbound transactions and financial data.