You're spending real money on accounting software that still makes you do the accounting. Every transaction needs a category. Every month-end needs reconciliation. Every board meeting needs a manual update to your burn rate. AI agents for finance invert that model. Instead of software that executes rules you set, you get software that learns your workflow, categorizes 85–90% of transactions automatically, and keeps your financials current without manual input between closes. The result is visibility that updates daily instead of monthly, reconciliation that takes five minutes instead of two hours, and books that stay audit-ready without last-minute scrambles. If your current stack still feels like a part-time job, these six tools are built for a different outcome.
TLDR:
AI agents for finance are software programs that go beyond simple automation. They perceive inputs like transaction data, invoices, and financial reports, reason through what they mean, and take action without waiting for a human to click a button. PwC reports 79% of executives say AI agents are already being adopted across their companies, a signal of how quickly the technology is moving into enterprise operations broadly.
Where traditional accounting software executes rules you set, AI agents make judgment calls. They can match and verify accounts, flag anomalies, draft journal entries, and update forecasts as conditions change. Research from MDPI shows AI agents reduce execution time by nearly 75% in accounting tasks.
For finance teams, this matters because the work is rarely about running a single task in isolation. AI Close handles full sequences. A closing process, for example, involves dozens of interdependent steps. AI agents can hold that context across the whole sequence.
We reviewed six AI agents for finance by putting each one through the same set of criteria a startup finance team actually cares about.
Puzzle's AI Close is purpose-built for startups running on a modern fintech stack. Founded in 2019 and trusted by 10,000+ companies, Puzzle was built AI-native from day one: the AI isn't a feature added later; it's how the product works.
The core of AI Close is a continuously running close checklist that executes on a schedule without requiring you to log in. Transactions get categorized automatically, reconciliation that previously took two hours now takes five minutes, and your burn rate and runway update in real time instead of waiting until month-end. For a deeper look at how to structure this workflow, see the guide on automate month-end close.
A few things make Puzzle worth calling out in this category:
Puzzle also partners exclusively with accounting firms instead of competing with them, which matters if you have a CPA or firm already in your corner.
Basis is an AI agent built for accounting firms, with a focus on automating the bookkeeping work that typically eats up staff hours. It connects to a client's accounts and categorizes transactions, flags anomalies, and prepares draft financials for a human reviewer to approve before anything is finalized.
The firm stays in control throughout. Basis is designed so that AI handles the repetitive work while accountants handle judgment calls, which fits squarely into a partner-first model instead of an autonomous-everything approach.
Basis works best for firms managing multiple client books who want to reduce per-client labor without sacrificing review quality.
Digits takes an autonomous-first approach to accounting: the AI acts, and you review what it already decided. For teams comfortable handing over the wheel, that speed is appealing. For founders who want to catch errors before they hit the books, that order of operations creates real exposure, a tension covered in depth in our piece on AI bookkeeping automation vs. judgment.
The tool handles transaction categorization, month-end close prep, and cash flow visibility reasonably well for early-stage companies. Where it gets complicated is the human-in-the-loop question. By the time you see the output, the decisions are already made.
Digits works best for founders who trust the AI implicitly and have an accountant reviewing downstream.
AI agents for finance typically fall into a few distinct categories, each built for a different part of the financial workflow.
The tools reviewed in this post span several of these categories. Knowing which stack you're operating in matters before choosing one. Two of the six tools reviewed here (Rillet and Campfire) are full ERPs rather than point-solution agents — they cover multiple stacks above but are architecturally distinct from the single-purpose agent tools in this list.
Rillet is an ERP built for high-growth startups, with a particular focus on revenue recognition and multi-entity consolidation. If your company runs complex SaaS billing models or operates across multiple legal entities, Rillet handles the accounting complexity that lighter tools struggle with.
Where Rillet earns its place is in the depth of its revenue recognition engine. For AI-native accounting software, the architecture matters. It automates ASC 606 compliance across variable billing structures, which matters once your ARR grows and your audit prep gets serious. Multi-entity consolidation happens in real time instead of waiting until close.
The tradeoff is scope. Rillet is sized for companies that already have a controller or a finance team in place. If you're pre-Series A without dedicated finance headcount, the setup overhead and pricing will likely outpace your actual needs. For a breakdown of what fits at each stage, see our guide to accounting agent software for startups.
Rillet competes in the same space as NetSuite at a lower price point, targeting companies that have outgrown startup accounting tools but are not yet large enough to need full ERP overhead. That's a real gap worth filling. Just make sure you're actually in that gap before buying into it.
Campfire is an AI-native ERP targeting mid-market tech companies that have outgrown QuickBooks and find NetSuite overkill. The product covers full general ledger, invoicing, billing, payroll, treasury, and multi-entity consolidation, built for companies with 50 to 500+ employees and a dedicated finance team already in place.
Campfire earns its keep at Series A and beyond, once you have a controller managing real complexity. For early-stage startups, the implementation overhead and pricing assume a level of finance infrastructure most founders won't have for years. If you're pre-Series A without dedicated finance headcount, you'll spend more time configuring an ERP than shipping your product.
The six tools compared across the criteria that matter most to startup finance teams.
| Feature | Puzzle AI Close | Basis | Digits | Stacks | Rillet | Campfire |
|---|---|---|---|---|---|---|
| Built-in GL | Yes | No | Yes | No | Yes | Yes |
| Requires Human Approval | Yes | Review at key points | Only when needed | Yes | Yes | Yes |
| Partner-Only Model | Yes | Not specified | No | Not specified | No | No |
| Custom Agent Builder | Yes | Yes | Yes | No | No | No |
| Month-End Close Focus | Yes | Yes | Yes | Yes | Yes | Yes |
| Startup-Specific Metrics | Yes | No | No | No | Yes | No |
| Multi-Entity ERP | No | No | No | Yes | Yes | Yes |
| Target Customer | Startups & Firms | Large Firms | Startups & Firms | Enterprise | Series B+ SaaS | Series A+ Tech |
| Pricing Model | Tiered subscription, publicly listed | Custom pricing, contact sales | Tiered subscription, publicly listed | Custom pricing, contact sales | Custom pricing, contact sales | Custom pricing, contact sales |
Tools with the most autonomy serve larger organizations with dedicated finance teams already in place to catch errors. For early-stage startups without a controller, human approval before anything hits the books is a real requirement, not a nice-to-have.
Puzzle AI Close was built for startups that need accurate, real-time financials without hiring a full accounting team: AI-native from day one, not retrofitted onto legacy architecture. Where most AI agents for finance bolt AI onto existing workflows, Puzzle built its entire accounting engine around AI from day one.
Puzzle closes books up to 50% faster than manual processes and works alongside accounting firms instead of replacing them, with 93% onboarding retention versus the industry average of 39%.
Puzzle was founded in 2019 in San Francisco to build AI-native accounting software for startups: not to retrofit AI onto legacy architecture, but to design the product around it from the start. Venture-backed and trusted by 10,000+ companies, Puzzle partners exclusively with accounting firms instead of competing with them.
Three factors separate the right tool from the wrong one: team size and entity complexity (an ERP built for multi-entity consolidation is overkill pre-Series A), degree of AI autonomy (tools that act before you approve create real exposure without a controller in place), and integration fit with your existing fintech stack (native connections to Stripe, Mercury, Ramp, Brex, and Gusto mean data flows in without manual exports). Match those three to your actual stage, and the shortlist gets short fast. If you want to see how Puzzle handles this without the overhead of an ERP, book a demo and we'll walk you through it.
Match your choice to your actual constraints: team size, entity count, and whether you have a controller. If you're single-entity with no dedicated finance headcount, you need automation that works without constant setup, not an ERP built for complexity you won't face for years. Look for tools sized for your stage that connect to your existing fintech stack and maintain human approval before anything hits the books.
Basis is purpose-built for firms managing multiple client books, with AI handling repetitive work while accountants retain control over judgment calls. Puzzle serves both: founders get real-time burn rate and runway visibility, while firms get a partner-only model where the software never competes for their clients. Autonomous tools like Digits work best for founders comfortable reviewing decisions after the AI has already acted.
AI-native means the product was architected from day one around AI: the system wouldn't deliver the same value without it. AI categorizes, matches transactions, and reviews accuracy continuously in the background without prompts. AI-added means the vendor bolted automation onto existing legacy architecture as a feature, which limits how deeply the AI can integrate with core workflows.
QuickBooks added AI through Intuit Assist: a layer of automation (bank rule suggestions, categorization nudges, invoice drafting) placed on top of architecture built in the 1990s. The underlying ledger, reconciliation engine, and close workflow were not designed for AI, so the automation handles isolated tasks but can't hold context across a full close sequence. You still approve each step manually, work through menus built for a pre-AI world, and manually clear accounts that the AI flagged but didn't resolve.
AI-native accounting software inverts that model. The ledger, categorization engine, and close checklist were built as a single system from day one. AI isn't a feature running on top; it's how the product executes. That architecture is what makes it possible to automate 85–90% of transaction categorization, run reconciliation continuously, and maintain a close checklist on a schedule without prompting. The practical result: QuickBooks with AI features still requires your time between closes. AI-native software keeps the books current while you're focused elsewhere.
For payroll providers with a native integration (Gusto, for example), Puzzle pulls payroll data directly and drafts the journal entries automatically. For providers without a native connection (ADP, Paychex, Rippling, and others), you can upload the payroll summary report or export as a PDF or CSV, and Puzzle's AI parses the document to draft the corresponding journal entries across payroll expense, employer tax, and benefit liability accounts. A human reviews and approves before anything posts. Even a non-integrated payroll provider doesn't require manual data entry: upload the report, review the AI-drafted entries, and approve. The more consistently your payroll provider formats its exports, the more accurately the AI maps line items to the right accounts over time.
If you're managing multi-entity consolidation, complex revenue recognition, or preparing for a Series B audit, an ERP like Rillet or Campfire starts making sense. Pre-Series A without a controller, the setup overhead assumes infrastructure most founders won't need for years.
Puzzle exposes an API that lets FP&A and strategic finance platforms pull real-time financial data (categorized transactions, trial balance, cash and accrual P&L, burn rate, and runway) for shared clients. If your platform serves clients who also use Puzzle for their books, the API means your models and dashboards stay current without manual exports or data reconciliation on your end. The integration is client-authorized: the shared client grants your platform read access, and your system queries Puzzle's endpoints on whatever cadence your product needs. For API documentation, authentication flows, and partnership discussions, reach out directly via puzzle.io; the team works directly with platform partners on scoped integrations.
Pricing varies widely by tool. Startup-focused tools like Puzzle offer transparent, tiered plans sized for early-stage budgets. ERP-style tools like Rillet and Campfire use custom quotes based on company size, entity count, and complexity: expect a sales conversation before you see a number.
Puzzle doesn't replace your payment collection layer; it integrates with it. If you're already invoicing through Stripe, Mercury, or Bill.com, Puzzle connects natively and pulls payment data in automatically, so revenue hits your books without manual entry. Here's how the options compare for clients moving from QuickBooks:
QuickBooks users typically juggle manual bank feeds, CSV imports, and rule-based categorization for each of these. With Puzzle, the connection is native: data flows in, gets categorized automatically, and posts with human approval. The switch takes days, not weeks, for single-entity startups on a standard fintech stack.
It depends on how the tool connects to your stack. Fintech-stack-native tools with pre-built integrations (Stripe, Mercury, Ramp, Brex, Gusto) can onboard in days. Multi-entity ERPs require weeks of configuration; the more structural complexity, the longer the setup.
A small number of platforms are architected to run journal entries and close sequences without waiting for manual triggers. Puzzle AI Close runs a continuously updated close checklist on a schedule: transactions are categorized automatically, reconciliation executes in the background, and journal entries are drafted and staged for human approval before anything is finalized in the ledger. The human-in-the-loop gate is intentional: it means the AI does the work of posting entries while a founder or accountant retains sign-off authority, which matters for audit readiness.
Digits takes a more autonomous approach: the AI acts and posts, and you review what it decided after the fact. That speed works for teams with a downstream accountant reviewing the output, but it creates exposure if no one catches errors before they compound across the month. Rillet and Campfire handle journal entries and close work inside a full ERP context, but those tools are sized for companies with a controller already in place; the journal entry automation assumes someone on your team owns the review process.
The practical distinction: platforms that post journal entries autonomously are either human-gated (AI drafts, human approves) or truly autonomous (AI posts, human reviews after). For early-stage startups without a controller, the gated model is the safer default.
AI Close runs a continuously updated close checklist on a schedule: you don't log in to trigger it. Each step in the checklist is handled by an agent: transaction categorization runs automatically against your connected accounts (Stripe, Mercury, Ramp, Brex, Gusto), reconciliation executes in the background, and journal entries are drafted and staged for human approval before anything is finalized in the ledger. Pre-built templates cover the most common close sequences for early-stage startups, including categorization rules, recurring accruals, payroll entries, and month-end tie-outs. AI credits are consumed per agent action; the number of credits used in a given close depends on transaction volume and the number of steps your checklist includes. Credits reset on your billing cycle, and your plan tier determines how many you get. For specifics on credit allocation by plan, reach out to the team directly via puzzle.io and they will walk through it for your volume.
Real results show measurable cuts: Puzzle closes books up to 50% faster, with reconciliation dropping from two hours to five minutes (96% faster). The savings come from automated categorization (85–90%), continuous daily reconciliation, and anomaly detection, but only when the tool maintains human approval gates before finalizing entries.
