Maxima vs LayerNext

Pricing Comparison

Two fundamentally different approaches to access and pricing.

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Key Difference: LayerNext publishes pricing and lets you validate the product against your own data before committing. Maxima requires a demo request for any access. For enterprise teams comparing options, that distinction matters.

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Feature Comparison

The pricing difference is significant and reflects completely different approaches.

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LayerNext: AI Finance Operations for Any ERP

LayerNext deploys AI agents that automate the full finance operation, not just the close layer. Agents capture invoices, reconcile bank feeds, close books continuously, enter data into ERP systems, and surface CFO-grade financial intelligence. The platform works on any system a human can use, including Windows-based desktop ERPs with no API access.

1. Custom Workflow Automation: Zero Code Required

Any automation can be configured without writing code. The team designs a workflow, a sequence of steps defining what the automation should do, and it is live. No development cycles. No testing sprints. No waiting on IT.

Example: adding an invoice to an accounting system requires designing a workflow that specifies what data to extract and how to verify it. The finance team manages it themselves.

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2. Entity-Wise Business Rules Engine

Every enterprise has specific processing rules for individual suppliers, customers, and entities. LayerNext includes a Business Rules section where teams add or edit any rule for a given entity, written in plain English. The rules engine uses a well-indexed search tree that retrieves the correct rule accurately even across thousands of active rules.

Example: if a different invoice processing rule applies to each of 200 suppliers, LayerNext retrieves the correct rule set by querying the supplier name. The right logic is applied to every transaction, without exception.

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3. AP and Invoices on Autopilot

Every invoice is captured, data extracted, validated against purchase orders, and routed for approval automatically. The agent processes end to end. The human approves.

This handles inconsistent formats, partial matches, and multi-vendor complexity that rules-based tools cannot manage. Manual AP processing time drops from 15 to 30 minutes per invoice to seconds. Three-way PO matching is automated.

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4. Legacy ERP and Desktop Automation: The No-API Differentiator

LayerNext agents navigate desktop applications the same way a human operator would: seeing the screen, identifying fields, entering data, and verifying results. No API setup. No middleware. No changes to existing infrastructure.

Systems LayerNext Operates On

• Epicor Eagle and Epicor Kinetic
• QuickBooks Desktop (Windows)
• Legacy Sage 100, Sage 300, Sage X3
• Microsoft Dynamics GP, NAV, and older AX versions
• SAP on-premise deployments
• Custom Windows-based ERP systems with no API
• Excel-based workflows

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5. Multi-Channel Data Input

Finance automation requires data from many sources: vendor bills, customer invoices, purchase orders, bank statements, accounting system records. In an enterprise, that data arrives from computer disks, shared folders, emails, ERP software, cloud storage, and databases. LayerNext feeds required data from any of these channels once set up in its automation workflows.

Supported Input Channels

• Dedicated email address for file ingestion
• Disk-based bot for feeding files from local computers
• Computer-use agent for retrieving data from accounting software
• Direct SQL database connectivity
• Cloud storage connections: AWS S3, Google Cloud
• API connectivity to accounting systems including QuickBooks

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6. Single Cloud Portal and Insight Board

Each enterprise gets a dedicated portal accessible by authorized staff. Every automation running across the enterprise is managed within this portal. The Insight Board gives managers a live view of automation processing statistics: transactions processed, pending clarifications, and overall automation health.

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7. Task-Based Human Review Queue

When LayerNext needs human input, it creates a task in the system, searchable by key data fields. Example: if an invoice has an incorrect tax for the shipped province, LayerNext creates a task asking the operator to verify the tax. The operator looks it up by invoice number and provides feedback directly. When all tasks are in a Done state, no human input is required for any active automation.

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8. Continuous Bank Reconciliation

LayerNext matches transactions against the ledger as they occur. Bank feeds connect directly or statements are uploaded. Discrepancies are flagged immediately. Books are always current.

• Bank feeds from Scotiabank, Bank of America, and others supported
• No month-end scramble: reconciliation is continuous
• Errors caught in real time, not discovered weeks later

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9. CFO-Level Intelligence

Once books are clean and current, LayerNext surfaces financial metrics that matter for operating decisions, updated in real time.

• Cash flow position and trend
• Burn rate and monthly spending patterns
• Runway: months of cash remaining at current burn
• Margin analysis by period or segment
• Tax-saving opportunities surfaced automatically
• Anomaly detection on unusual transactions
• Enterprise: custom dashboards, variance analysis, management reports by KPI

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Maxima: AI Close Automation for Tech-Forward Accounting Teams

Maxima is an AI-native accounting platform built for the financial close. It automates the record-to-report process, from journal entry preparation through reconciliation, flux analysis, and variance explanation, for accounting teams at technology companies with modern cloud infrastructure. Backed by Kleiner Perkins and Redpoint ($41M), Maxima processes over $300 billion in transaction volume across 150+ native integrations.

1. Finance Graph: Unified Data Model

Maxima's core architecture is a finance graph that pulls data from every upstream system into a single model of context and lineage. This enables agents to prepare accounting work with full evidence, linking every output back to the source transaction. Native integrations include Workday, Salesforce, Stripe, Charles Schwab, Excel, NetSuite, and 150+ modern enterprise systems. All connections are API-based.

2. Agent-Prepared, Accountant-Reviewed

Agents prepare every output, including journal entries, reconciliations, and variance explanations, and present them for accountant review. The accountant approves or rejects with one click. This accelerates close without removing human judgment from high-stakes decisions.

Customer Outcome (Scale AI CAO)
Closing two to three days faster with over 98% automation.

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3. Journal Entry Automation

Maxima automates the full spectrum of recurring journal entries:

• Cash coding journal entries
• Payroll journal entries
• Commission accruals
• Stock-based compensation and cap table journals
• Prepaid expenses and amortization schedules
• Lease accounting and depreciation schedules
• Allocation journal entries across entities and cost centers

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4. Balance Sheet Reconciliation

Maxima automates balance sheet reconciliation across entities: matching sub-ledger to GL, flagging unreconciled items, and generating variance explanations. The audit trail is 100% re-performable, meaning every reconciliation can be reconstructed from source data for any audit.

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5. Flux Analysis

Explaining why account balances moved period over period is one of the most time-consuming close tasks for accounting teams. Maxima syncs data automatically and lets controllers investigate variances at the vendor or transaction level in seconds.

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6. Evidence-First Audit Trail

Every agent-prepared output includes full lineage: inputs used, policies applied, approvals obtained, and the source transaction reference. Designed specifically for SOX-compliant environments and Big Four audit readiness.

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7. Maxima Enterprise Customer Profile

• SaaS, fintech, and tech companies with 50 to 5,000 employees
• Finance stacks built on Workday, NetSuite, Salesforce, Stripe
• Dedicated accounting teams: Senior Accountants, Controllers, CAOs
• SOX-compliant environments requiring full audit trail
• US-based companies looking to close 2 to 3 days faster on a modern stack

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ROI: What Gets Replaced and What It Costs

The ROI question is different for each platform. For Maxima, the question is: how many accounting team days does the close consume each month? For LayerNext, the question is simpler: how many people are manually processing invoices and entering data into your ERP right now?

For most mid-market distribution and manufacturing companies, that answer is 6 to 8 data entry clerks at $50K to $60K per year each. That cost is already on the books. LayerNext replaces it.

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US and Canadian Business Considerations

LayerNext: Built for Both US and Canadian Enterprises

LayerNext operates from two headquarters, one in the United States and one in Canada, and serves businesses in both markets directly. The US headquarters is at 235 Berry Street, San Francisco, CA 94158, and the Canadian headquarters is at 200-135 Innovation Drive, Winnipeg, MB R3T 6A8. This dual presence means LayerNext supports US and Canadian finance operations natively, rather than treating either country as an afterthought, and is equally at home with an operational business in the US or a distributor in Canada.

• Two headquarters: San Francisco (US) and Winnipeg (Canada), serving both markets directly
• USD and CAD currency support, with US and Canadian bank feeds from institutions including Scotiabank and Bank of America
• Manitoba AI Innovation Award winner, Leadership in AI, March 2026
• Anchor enterprise customer: a national building materials distributor with 25+ distribution centers across Canada
• Active US and Canadian small business support across the self-serve segment

Maxima for US and Canadian Businesses

Maxima is a US-based company with no disclosed Canadian office, Canadian-specific operations, or Canadian customer references. All named customers and testimonials are US-based technology companies.

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Who Should Use Each Platform?

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Why We Built LayerNext

As founders, we kept running into the same problem in the companies we built and advised: the finance team was bottlenecked by manual work that no available software could actually automate.

For tech companies on Rippling or NetSuite, tools like Maxima solve the close layer well. But the majority of mid-market companies, distributors, manufacturers, and building materials companies, run on Epicor, older Sage, or custom Windows ERPs. No API. No modern connectors. No path to automation with existing tools. And no way to configure supplier-specific business rules without waiting months for a developer.

We built LayerNext to solve the harder problem: automate finance operations for companies that existing platforms cannot reach. Computer-use automation means our agents navigate desktop ERPs like a human operator. A zero-code workflow engine means finance teams configure their own automations. Plain-English business rules mean entity-specific logic is set up in hours, not development sprints.

One product, two go-to-market channels, one mission: every business deserves CFO-level understanding of its financial health.

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Final Verdict

Maxima is a well-executed product for a specific enterprise buyer: accounting teams at technology companies with clean API infrastructure, modern cloud ERPs, and a need to close books faster. The Scale AI outcome, closing 2 to 3 days faster with 98% automation, is real and credible. For that buyer, Maxima earns a serious evaluation.

The majority of mid-market companies are not that buyer. Distribution, manufacturing, logistics, and building materials companies run on Epicor, legacy Sage, older Dynamics, and custom Windows ERPs built 15 to 20 years ago. These systems have no API. Every existing finance automation platform, Maxima included, stops at the door of those environments.

LayerNext is the only platform built to operate inside them. Computer-use automation means the agent navigates the ERP the same way a finance clerk does, with no API required and no infrastructure changes. A zero-code workflow engine and plain-English business rules mean the finance team owns the automation, not the development team. And the ROI is direct: 6 to 8 data entry clerks at $55K each, displaced by a platform that costs $25K to $100K per year. The math does not require a spreadsheet.

If your stack is modern and your primary pain is close speed, evaluate Maxima. If your environment is legacy, your team is manually processing high AP volumes, and you need CFO-grade visibility on top of clean books, LayerNext solves a problem no other platform currently addresses.