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 up to 98% 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 adopted in their companies.
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. Where most AI agents for finance layer automation onto existing workflows as an afterthought, Puzzle was built AI-native from day one, which means 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.
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.
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.
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.
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 company raised over $100M from Accel and YC in 2025, and the product shows it: 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 | SMBs | Enterprise | Series B+ SaaS | Series A+ Tech |
The pattern is worth naming: tools with the most autonomy tend to 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. Where most AI agents for finance bolt AI onto existing workflows, Puzzle built its entire accounting engine around AI from day one.
A few things make Puzzle worth serious consideration if you're an early-stage startup:
That last point matters. Puzzle was designed to work alongside accounting firms, not replace them. The AI handles the repetitive volume; your accountant handles judgment calls and advisory work.
For startups preparing for a fundraising round or trying to cut month-end close time, Puzzle closes books up to 50% faster than manual processes, with 93% onboarding retention compared to the industry average of 39%.
The difference between AI agents for finance comes down to control, fit, and whether the tool was built for your stack or retrofitted onto legacy architecture. For early-stage startups, real-time visibility and human approval before anything hits the books aren't nice-to-haves. 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.
If you're managing multi-entity consolidation, complex revenue recognition across variable billing structures, or preparing for a Series B audit, an ERP like Rillet or Campfire starts making sense. Before that point, especially pre-Series A without a controller, the setup overhead and pricing assume infrastructure most founders won't need for years, and you'll spend more time configuring than shipping product.
Real case studies show measurable cuts: Puzzle closes books up to 50% faster than manual processes, with reconciliation dropping from two hours to five minutes (96% faster). The time savings come from automated transaction categorization (up to 98% in some cases), continuous reconciliation that runs daily instead of only at month-end, and anomaly detection that flags errors before they reach reports, but only if the tool maintains human approval gates before finalizing entries.
