DualEntry’s $90M Wager: How AI Aims to Unmake the Monolith of Enterprise Accounting

When legacy enterprise resource planning systems were first sold as the backbone of corporate finance, they promised a single source of truth. Instead they often delivered years of technical debt: bespoke customizations, brittle integrations, sprawling data models and month-end rituals that feel more like archaeology than accounting.

Now, with a freshly closed $90 million Series A, a company called DualEntry is betting that artificial intelligence can do something enterprise software vendors have struggled to achieve for decades: not merely bolt new features onto a creaking stack, but rewrite the rules of accounting workflows from the ground up. This is not about an incremental plugin. It is a claim that the accounting layer itself can be reimagined — faster, more adaptive, and less dependent on the ossified architectures of the past.

A tectonic problem: why ERPs endure, but frustrate

ERP vendors like to sell permanence. They offer stability at the cost of agility. The reality in the field is different: every large corporation ends up with a customized ERP instance, a forest of middleware, and a compliance regime wired to the quirks of that setup. Financial teams spend massive hours on reconciliation, corrections, and fighting schemas. Month-end closes, audits and manual journal entries are routine time sinks.

ERP systems persist because migrating books and processes is risky and expensive. Yet those same costs create an opening: a sufficiently capable alternative that reduces risk, improves transparency, and accelerates financial cycles can displace incumbents not by force, but by delivering better economics and faster insights.

The DualEntry thesis: replace the workflow, not just the UI

DualEntry’s pitch is audacious but simple. Use AI to automate the laborious, rules-heavy work that lives between transactions and ledgers: document interpretation, mapping to accounts, validating business context, creating journal entries, and continuously reconciling ledgers. The goal is to convert episodic, manual accounting into a continuous, observable process.

Under the hood, the approach combines several modern techniques:

• Large language models and domain-tuned encoders to understand invoices, contracts, and free-text entries and translate them into structured accounting facts.
• Retrieval-augmented generation (RAG) over a knowledge base of company policies, chart of accounts, tax rules and past audit decisions so that model outputs are grounded in corporate policy and precedent.
• Knowledge graphs and semantic mappings to reconcile disparate data schemas and maintain a canonical model of customers, vendors and ledgers.
• Deterministic rules engines layered with probabilistic models to capture hard compliance constraints while letting AI handle messy exceptions.
• Immutable audit trails and reproducible pipelines so every generated journal entry is traceable to source documents, model prompts and governance approvals.

From coexistence to replacement: a pragmatic migration path

Displacing an ERP overnight would be reckless. DualEntry’s narrative recognizes this and emphasizes staged replacement:

• Coexistence connectors: two-way syncs with existing ERPs to let teams adopt AI-driven automation incrementally while preserving legacy ledgers.
• Selective automation: automate high-volume, low-risk transaction types first — e.g., standard vendor invoices, bank reconciliations — then expand to more complex activities as confidence grows.
• Human-in-the-loop: domain experts review model suggestions where risk or judgment is high, and their corrections continuously retrain models to reduce future intervention.
• Data migration tooling: semantic mapping utilities translate legacy schemas into the canonical accounting model, reducing the manual work that normally kills migration projects.

Trust, auditability and regulatory friction

The accounting profession runs on trust and verifiability. Any AI-driven contender must answer three essential questions: can you demonstrate how a figure was produced, can you guarantee compliance with standards like GAAP or IFRS, and can you produce incontrovertible evidence for auditors?

DualEntry’s architecture responds with layered safety: explainable model outputs tied to source artifacts, signed audit logs that preserve the exact model version and data snapshot used to produce any recommendation, and a hybrid engine that refuses to override hard-coded regulatory constraints. In essence, the company is rebuilding the audit trail for an era when decisions are co-authored by software and humans.

Human impact: re-scripting the role of finance

Automation will change jobs — sometimes gradually, sometimes suddenly. The most likely outcome is a redistribution of effort: routine data entry and reconciliation decline while analysis, control design, exception management and strategy become central. Finance teams will need new skills: model governance, data stewardship, and the ability to interrogate AI outputs. For companies that manage the transition well, finance can become a faster, more strategic function.

There is another consequence: by reducing the friction of generating trusted financial information, firms gain the ability to make decisions in near real time. That changes corporate rhythm — forecasting becomes continuous, capital allocation more agile, and risk management more proactive.

Market dynamics: incumbents, partners, and the ecosystem

Legacy ERP vendors won’t surrender market share quietly. Expect a mix of responses: deeper AI integrations in incumbent stacks, acquisition to shore up capabilities, and pricing pressure. Meanwhile, cloud providers and middleware companies will vie to be the preferred platform for DualEntry-style systems.

If DualEntry’s model proves durable, it could accelerate a shift toward composable finance stacks: best-of-breed AI accounting engines paired with specialized subsystems for procurement, payroll and treasury, rather than a single monolithic suite. That creates opportunities for partners — analytics firms, controls auditors, and vertical specialists — to build on top of a standardized, AI-native accounting layer.

Risks and the hard limits of automation

AI is not a panacea. Hallucination remains a risk, particularly in ambiguous or adversarial financial data. Poorly curated training data can bake in biases that distort financial reporting. There are also geopolitical and privacy constraints: cross-border data flows, local tax regimes and differing disclosure rules complicate a one-size-fits-all model.

Mitigation requires rigorous validation, continuous model evaluation, robust access controls, and clear escalation paths for human reviewers. Companies that cut corners will court failures that could set back adoption for the entire field.

Why $90M matters

Series A rounds this large signal two things: capital intensity and ambition. Replacing foundational enterprise systems requires not only algorithmic innovation but also engineering to scale, rigorous security and compliance work, global sales and integration teams, and time. The new capital gives DualEntry runway to build connectors, certify compliance frameworks, acquire customers in multiple industries, and harden their platform against the kinds of edge cases that sink enterprise projects.

The long view: what displacement looks like

If DualEntry and similar companies succeed, the accounting landscape will change in measurable ways:

• Shorter close cycles — days to hours — and continuous financial statements.
• Reduced manual effort across reconciliations, accruals and intercompany accounting.
• New organizational roles around model governance and financial data engineering.
• Composability in finance tech stacks, enabling faster innovation and lower integration costs.

Displacement of legacy vendors will be uneven. Large, mission-critical systems tethered to global operations won’t vanish immediately. But the new entrants need only carve out enough capability and trust to become the preferred choice for the next generation of finance systems. Over time, the economics of automation and the agility it enables will do the rest.

Closing: accounting as an information system, reimagined

DualEntry’s $90 million bet is not just about faster reconciliations or prettier dashboards. It imagines accounting as an active, living information system: one that interprets, validates and synthesizes economic reality continuously. In that world, finance becomes less about looking back and more about steering forward. That is a bold proposition, and it is exactly the sort of disruption that has historically toppled software incumbents: superior economics married to a fundamentally better workflow.

Whether DualEntry will be the company to complete that transformation remains to be seen. What is clear is that the contours of enterprise finance are changing. AI, when applied with rigor and humility, may finally give companies the agility their balance sheets have long craved.