Accounting solutions provider
The HpAI engine is specifically for accounting tasks, with semi-autonomous agents built into the very core of the product. These agents handle tasks like auto-categorizing transactions, reconciling accounts, tabulating data, preparing analytics, flagging anomalies and closing the books in real time, tasks typically handled by junior accountants.
"When we talk about agents, we're referring to the intelligent components embedded within the product that power Docyt's automation," said Docyt co-founder and CEO Sid Saxena in an email. "They're not an additional service or optional module—they are the mechanism that delivers Docyt's automation capabilities. Think of them as the behind-the-scenes workforce continuously working to eliminate manual bookkeeping tasks and accelerate financial workflows."
Docyt said the engine itself is industry-agnostic, covering core bookkeeping tasks for any business, but can then be loaded with specialized industry packs that add further capacities for specific verticals. The first is a Hospitality Industry Pack that also includes features such as USALI reporting, hotel metrics such as revenue per available room and average daily rate, and tailored workflows and dashboards. Saxena said additional packs, including one for quick-service restaurants, are coming soon.
Saxena said the HpAI foundation models were trained on Docyt's own curated corpus, which includes a large synthetic bookkeeping dataset created by the company's in-house bookkeepers, along with public accounting taxonomies and benchmarks. Docyt said the models do not require retraining on customer data as they adapt in real time by retaining contextual information in memory to deliver customer-specific precision. The CEO further stressed that client data from accounting firms is used only at runtime for contextualization in memory and is never used to train or fine-tune the HpAI base models.
Asked how the firm ensures accuracy and consistency in its AI outputs, Saxena cited "human-in-the-loop" review during the final closing stage, supported by an auxiliary HpAI suite for verification; as well as drift detection and regression testing with every HpAI suite release; versioned datasets and models to maintain traceability and auditability; and a curated synthetic bookkeeping corpus with strict schema validation and data quality enforcement.
This product launch follows Docyt's recent pre-Series B funding round, which added $12 million in new capital led by Pivot Investment Partners and existing investors. The funding will help scale Docyt's AI solutions across accounting firms and multi-entity businesses.
"At Docyt, we have spent over five years building high-quality synthetic datasets using in-house expertise of expert accountants doing high-quality data labeling that captures the complexity of real-world bookkeeping. This dataset is a critical component of our HpAI architecture, enabling it to deliver precise, context-aware automation," said Saxena in a statement. "We are now bringing this foundational AI architecture to accounting firms in the form of Docyt AI Copilot, enabling them to deliver client bookkeeping at scale, and with precision."
