Puzzle’s AI-powered general ledger is redefining how organizations handle core finance work by embedding complex accounting logic directly into the backbone of their financial systems. This shift aims to automate up to 90% of routine tasks, freeing accountants to concentrate on strategic decision-making, risk management, and advisory capabilities. The concept centers on moving the heavy lifting of accounting policy, treatment, and calculations away from scattered spreadsheets and into a centralized platform that can interpret, apply, and audit rules in real time. In a detailed conversation with VentureBeat, Puzzle’s CEO, Sasha Orloff, outlined how the new general ledger integrates sophisticated accounting policies into the core software, with the explicit goal of reducing or even eliminating manual spreadsheet processes. The platform is designed to handle both cash and accrual accounting, making it relevant to businesses of all sizes and maturity levels, and it emphasizes real-time accuracy to meet the demands of today’s fast-paced corporate environments. The broader context is clear: as the accounting industry contends with talent shortages and rising workloads, a system that can automate routine, repetitive tasks while maintaining control and transparency could reshape how firms scale and compete.
Puzzle’s AI-powered general ledger: Automating the backbone of accounting
Puzzle’s general ledger stands at the intersection of traditional accounting discipline and next-generation automation. The core proposition is straightforward in its ambition: replace the most tedious, error-prone spreadsheet-driven tasks with a centralized, intelligent engine that can encode complex policies, apply them consistently, and produce auditable results with minimal human intervention. Orloff emphasizes that the platform isn’t merely a workflow tool; it represents a structural shift in how the ledger—the backbone of all financial reporting—operates. By bringing complicated accounting logic from spreadsheets into the general ledger itself, the system aspires to deliver real-time insights and tighter control over financial data. This approach is designed to support organizations of varying sizes, recognizing that both small businesses and large enterprises require robust accounting processes that still align with their unique policy requirements and reporting needs.
A crucial feature of Puzzle’s platform is its support for both cash and accrual accounting. This dual capability ensures that businesses with different financial calendars, revenue recognition rules, or asset management practices can rely on a single, consistent engine to produce accurate results. Orloff notes that the system is engineered to respond quickly to the changing demands of modern finance functions, where speed and precision are increasingly non-negotiable. The solution promises real-time processing and updates, enabling finance teams to see the impact of transactions as they occur and to adjust strategies accordingly. This level of responsiveness is particularly valuable in a landscape characterized by rapid business cycles, volatile markets, and a persistent mismatch between demand for timely insights and the capacity of traditional accounting teams to deliver them.
The automation target includes tasks such as revenue recognition, asset depreciation, and prepaid expenses—areas historically nourished by spreadsheet models and manual reconciliations. Puzzle’s approach seeks to streamline these processes by encoding the relevant rules and calculations into the platform, so they apply automatically when invoices come in or when a bill is entered. In practice, this means less time spent cross-checking numbers across multiple tools and fewer opportunities for human error to derail the close process. Orloff explains that the platform reduces the frequency and severity of reconciliation gaps between accounting software like QuickBooks and spreadsheet-based worksheets, creating a more coherent, end-to-end financial picture.
To operationalize these capabilities, Puzzle enables users to establish “software-driven accounting policies” for different transaction types. These policies capture business logic for, for example, SaaS subscriptions or prepaid contracts. Once a policy is defined and saved, it automatically triggers whenever the relevant transaction is recorded, ensuring consistency and reducing repetitive manual input. The intended payoff is a notable drop in manual data entry, a lower error rate, and faster close cycles, all while preserving the opportunities for human review and oversight when necessary. In Orloff’s view, this combination—policy-driven automation plus flexible human oversight—creates a foundation for increased accuracy, efficiency, and reliability across the financial close process.
Real-time insights and policy-driven automation
The platform’s capacity for real-time processing means that companies can observe the implications of transactions as they occur, rather than waiting for a batch reconciliation. This immediate feedback loop supports more agile financial planning and analysis, enabling organizations to respond to emerging financial trends quicker and with better data. The policy-driven aspect provides a level of governance that helps ensure compliance with internal standards and external regulations, reducing the risk of inconsistent treatments or inconsistent reporting across periods. The system’s design highlights a broader aim: to bridge the gap between the precision demanded by accounting standards and the practical realities of business operations, where policy interpretation often becomes a bottleneck in manual workflows.
Human-in-the-loop and transparency: balancing automation with accountability
A central concern with any AI-driven automation, particularly in the finance domain, is accuracy. Puzzle addresses this through a deliberate human-in-the-loop approach that gives accountants control over the degree of automation applied to each transaction. Orloff describes a flexible framework in which users determine whether the system should “take its best guess” and present results for later review, or whether the user prefers to handle the decision manually. In other words, the accountant always retains ultimate responsibility, with the AI offering suggestions, calculations, and classifications that can be accepted, modified, or rejected.
Transparency is further strengthened by transaction tagging. Each entry is labeled with metadata that indicates the basis for its processing—whether the result was generated by AI, or whether it is a high-confidence outcome drawn from prior behavior and established patterns. This tagging provides a clear trail that auditors—whether internal or external—can follow to understand how numbers arrived at their final state. The system’s approach to tagging and traceability is designed to support audit readiness and to provide the clarity needed for confident decision-making in financial leadership roles.
The emphasis on human oversight also directly addresses concerns about AI “hallucinations”—incorrect or implausible suggestions that can occur with AI systems. Orloff acknowledges the risk of AI-driven outputs being inaccurate, but he frames the solution as an environment where AI performs initial analyses and recommendations while humans verify and control every step. By combining AI-assisted insight with conscious human review, Puzzle aims to mitigate the risk of errors while still reaping the efficiency gains of automation. This balanced approach reflects a recognition that AI can accelerate routine work and support judgment, but it cannot entirely replace the nuanced understanding and professional skepticism that finance professionals bring to the table.
Accountability through traceability and control
In practice, the system’s design ensures that accountants can trace each transaction’s origin and treatment back to a defined rule or policy. This traceability is essential for both internal governance and external audit requirements. The ability to review how a transaction was drafted, whether it was AI-generated or manually confirmed, and whether the rule that applied originated from a policy embedded in the platform adds a layer of accountability that is often missing in spreadsheet-driven workflows. It also helps reduce the cognitive load on accountants by providing a clear framework for decision-making, enabling them to focus on higher-value tasks rather than repetitive data entry or error-prone reconciliation tasks.
Addressing the accounting talent shortage with AI
Puzzle’s launch arrives at a moment when the accounting profession faces acute talent constraints that threaten to slow business growth and strain client service levels. The industry has long depended on a steady stream of skilled professionals, but current conditions are changing the calculus. The platform is positioned as a strategic tool to augment the capabilities of existing staff, enabling firms to scale their client bases without proportionally increasing headcount. Orloff frames Puzzle as a catalyst for elevating the role of accountants rather than replacing them, arguing that AI can take over repetitive routines and free up professionals to contribute more strategic value.
Several data points underscore the severity of the talent gap. According to Orloff, 75% of accountants are nearing retirement, suggesting a looming wave of attrition that could exacerbate backlogs and slow adoption of advanced tools. He also cites estimates that 300,000 accounting professionals have left the workforce, while CPA applications have declined by roughly 30%. Burnout remains pervasive, with 99% of accountants reporting fatigue linked to the repetitive nature of their daily tasks. Taken together, these indicators paint a vivid picture of a profession strained by workload, aging workforce, and limited supply of new entrants to replace retiring practitioners.
Against this backdrop, Puzzle positions itself as a practical response to these pressures. The aim is to enable more efficient processing, faster periods close, and more capacity to handle a growing client roster without a corresponding rise in staff. In Orloff’s estimation, the automation solution can alleviate some of the stressors tied to repetitive, error-prone tasks, letting professionals devote greater attention to high-value activities such as analysis, forecasting, tax strategy, and financial health optimization. He contrasts Puzzle’s approach with other products and campaigns that advocate for reducing the human workforce; instead, Puzzle seeks to empower accountants and accounting firms to deliver higher-quality service with improved margins and satisfaction.
Growth trajectory and market adoption
Since its public debut less than a year ago, Puzzle has already demonstrated meaningful traction in a way that aligns with its value proposition of automation and strategic enablement. The company reports processing more than $30 billion in transactions across a diverse base of over 3,000 businesses. Its customer mix spans startups, small businesses, and mid-market entities, with notable usage among platforms and tools such as Stripe, Gusto, and Brex that indicate an ecosystem-friendly approach. The growth trajectory appears consistent with a business model designed to scale through automation-enabled margins and broader client acceptance of AI-assisted accounting.
Orloff attributes much of Puzzle’s momentum to word-of-mouth growth, highlighting a rise in month-over-month expansion that has ranged between 15% and 20%, with roughly 70% of that growth described as organic. This suggests that existing users find value in the platform’s capabilities and expand their usage or bring on additional transactions as their operations mature. While the initial traction skewed toward startups—where modern toolchains and an openness to new accounting paradigms create ripe conditions for early adoption—the company reports a notable shift in demand toward small businesses and accounting firms. Doctors’ offices, law firms, and retail stores have entered the pipeline, signaling a broader demand for AI-driven automation that can adapt across various professional services and sectors.
From an enterprise perspective, accounting firms in particular stand to gain from Puzzle’s automation. Firms facing staff shortages can manage larger client portfolios while maintaining service quality and margins. Orloff suggests that clients can be served at higher levels of accuracy and speed, enabling firms to take on more work without proportionally increasing headcount. This dynamic is especially compelling in markets where talent scarcity constrains growth and where clients demand faster, more reliable financial insights. The economics of reduced manual labor, combined with improved client satisfaction and the potential for higher-value advisory work, create a compelling argument for adoption.
Early outcomes and broader impact
The practical outcomes observed by early adopters include improved accuracy, reduced cycle times for monthly closes, and enhanced visibility into transaction-level details. The human-in-the-loop design helps maintain confidence among finance teams that the system’s recommendations align with their standards and policies, while still providing a straightforward path for correction when necessary. For accounting firms, the ability to scale client bases without proportionally increasing staff translates into higher operational leverage and the potential for more predictable capacity planning. The system’s tags and audit trails also lend themselves to stronger governance and compliance postures, which are crucial in regulated industries and in client engagements requiring rigorous documentation of treatment and methodology.
As Puzzle continues to mature, the platform is positioned to evolve along with the broader financial and regulatory landscape. Real-time accounting, policy-driven automation, and transparent AI-assisted workflows are consistent with longer-term trends in enterprise software where intelligent automation supports strategic decision-making. The company’s stated belief is that the next phase in accounting will resemble a shift from routine bookkeeping tasks to advisory, where professionals leverage AI-generated insights to improve tax strategies, cash management, risk assessment, and overall financial health. In this vision, the accountant’s role gains strategic prominence, and automation handles the repetitive groundwork that historically consumed much of the workday.
AI as a strategic driver for the future of accounting
Orloff frames Puzzle as more than a product—it is a signal of a broader evolution in accounting practice. When reflecting on the historical impact of technological innovations such as Excel, he notes a pattern: the elimination of certain low-value roles coincides with the creation of new, higher-value positions. In the case of Excel, many bookkeeping tasks were automated, but the market simultaneously expanded opportunities for advisory roles that required interpretive judgment and strategic guidance. Orloff argues that a similar transformation is underway today with AI-driven general ledgers. Repetitive, rule-based tasks can be automated, enabling accountants to focus on more meaningful contributions such as strategic analysis, tax optimization, and improving the financial health of organizations.
Puzzle’s perspective emphasizes the value of a symbiotic relationship between humans and machines. AI can handle the repetitive, data-intensive tasks, but human professionals bring the nuanced reasoning, ethical considerations, and strategic judgment that machines cannot replicate. The platform’s design—featuring controlled automation, transparent AI outputs, and auditable processes—aims to preserve trust while delivering speed and scale. The envisioned future is one where accountants are empowered to participate more deeply in critical financial decisions, supported by AI-driven insights that illuminate scenarios, risks, and opportunities that might otherwise remain hidden in spreadsheets.
The envisioned workflow shifts responsibilities from the back office to a more strategic seat at the table. If automation can take care of routine activities, accountants can devote more attention to high-impact work, such as devising tax strategies, improving financial health, and advising management on capital allocation and risk management. This transition has implications for compensation structures, career pathways, and the skill sets valued in the profession. Puzzle’s leadership believes this could lead to higher compensation for engineers and analysts working in finance-solution spaces, while also elevating the strategic stature and visibility of accountants within organizations.
A flywheel of efficiency and accuracy
As adoption grows, Puzzle foresees a self-reinforcing cycle: more users generate more data, enabling the platform to enhance automation capabilities and improve accuracy. This virtuous cycle—often described as a flywheel effect—would accelerate learning, refine policy applicability, and broaden the scope of use cases across industries. With each additional transaction and client, the system’s models encounter new patterns, enabling better predictions, smarter classifications, and more reliable outputs. The prospect of continuous improvement aligns with the goal of delivering consistent value to businesses, accountants, and firms alike, while maintaining the human oversight that safeguards integrity and accountability.
In this broader context, the platform’s ongoing evolution is not simply about efficiency gains; it’s about enabling a qualitative shift in how finance organizations operate. The potential for AI to support more informed decision-making, tighter governance, and improved financial health has implications for corporate strategy, risk management, and regulatory compliance. Puzzle’s roadmap and messaging suggest a future in which AI-powered automation is a core differentiator for firms that want to deliver superior client service, better margins, and more strategic collaboration between accountants and business leaders.
Practical use cases and integration landscape
Within the wider ecosystem of financial software and services, Puzzle’s general ledger is designed to complement and integrate with existing tools. The platform’s architecture appears to accommodate common accounting workflows and popular software stacks, enabling practitioners to leverage AI-driven automation without abandoning familiar interfaces. By embedding policy logic into the ledger, the system can standardize treatments across a range of transactions and contracts, reducing the need for bespoke spreadsheet models that often diverge between departments or clients. This standardized approach helps create more consistent outputs, which in turn simplifies consolidation, reporting, and audit processes.
A key advantage for early adopters is the ability to manage larger client portfolios with the same or smaller teams. Firms can reallocate resources toward advisory services and higher-margin engagements rather than routine data entry and reconciliation tasks. End users can expect a smoother close process, fewer manual adjustments, and more reliable financial statements due to the platform’s built-in governance and traceability features. The result is not just faster accounting but also an improved experience for clients who demand timely, accurate, and insightful financial information.
In addition to core ledger functionality, Puzzle’s platform supports a broader array of use cases that can scale across different industries. The system’s flexibility to handle various revenue models, asset classes, and contractual arrangements makes it applicable to SaaS companies, manufacturing entities, professional services firms, and other sectors that rely on precise revenue recognition and asset management. The platform also positions itself as a potential partner in regulatory compliance efforts, given its emphasis on transparency, policy-driven operations, and audit-ready outputs. As organizations navigate evolving accounting standards and reporting requirements, Puzzle’s approach offers a pathway to maintain consistency and accuracy across periods and entities.
Adoption challenges and considerations
Despite the compelling value proposition, widespread adoption of AI-powered general ledgers will require careful change management, onboarding, and governance. Finance teams must be prepared to define and codify accounting policies within the platform, ensuring that the rules accurately reflect business practices and regulatory standards. There may be initial learning curves as users become proficient in configuring policies, interpreting AI-generated outputs, and establishing appropriate review thresholds. Organizations will need to invest in training and to design controls that balance automation with oversight in line with their risk tolerance and compliance requirements.
Security and data governance are perennial concerns when deploying AI-enabled financial systems. Enterprises must verify that data flows are secure, access controls are robust, and that sensitive information remains protected throughout processing and storage. Clear data lineage, audit trails, and role-based permissions become essential to maintaining confidence in the system and ensuring that internal controls remain effective. As with any enterprise deployment, stakeholders will weigh the cost of adoption against the anticipated efficiency gains, improved accuracy, and enhanced strategic value that comes from AI-assisted accounting.
Conclusion
Puzzle’s introduction of an AI-powered general ledger marks a significant milestone in the ongoing evolution of accounting practices. By embedding complex accounting policies directly into the core ledger, the platform aims to automate a majority of routine tasks, reduce reliance on error-prone spreadsheets, and deliver real-time, accurate insights that align with the pace of modern business. The human-in-the-loop design preserves professional judgment and governance, offering accountants the flexibility to supervise automation while benefiting from AI-driven support. The timing of the launch sits squarely in a period of acute talent shortages, high burnout, and a growing demand for scalable, high-quality financial operations. With the platform already processing tens of billions of dollars in transactions across thousands of businesses, Puzzle is positioned to reshape how firms scale, how accountants partner with business leaders, and how the profession evolves toward more strategic, value-added work.
The broader implications extend beyond immediate efficiency gains. If the AI-enabled general ledger proves durable and scalable across industries, it could catalyze a broader shift in accounting—paralleling past transformations driven by earlier technologies like Excel—where routine tasks are automated, and skilled professionals focus on advisory, strategy, and financial health optimization. The envisioned future is one in which AI-assisted tools augment human expertise, enabling more strategic conversations, stronger client relationships, and better outcomes for the economy as a whole. As more businesses adopt Puzzle, the resulting cycle of increased automation, greater accuracy, and expanded capabilities will continue to reinforce the benefits across the accounting ecosystem, creating a pathway toward higher professionals’ impact, improved margins for firms, and a more efficient, resilient financial infrastructure.