Digits

Four Years of Cleanup in Four Hours: Inside the AI Ledger That Learns From Your Work

Earmark Team · January 15, 2026 ·

What if you could stop programming bank rules forever? No more tweaking text strings, adding exceptions, or debugging why “COSTCO WHSE #1234” won’t match your Costco rule. During a recent Earmark Expo webinar, accounting software company Digits demonstrated exactly how that future works, and they achieve 96% automatic categorization accuracy without a single bank rule.

Host David Leary has been watching Digits since before ChatGPT existed. “I remember seeing a pitch deck about Digits, and it was being emailed around on backchannels in the accounting industry,” he recalled. “This pitch deck was super ambitious. At the time, the back channel hallway talk was like, ‘Great, here comes another bank feeds accounting app.’”

Now, years later, that ambitious vision is reality. Rob Hamilton from Digits’ partnerships team showed David and his co-host Blake Oliver what the company calls the world’s first “agentic general ledger,” software built from the ground up with machine learning at its core.

Why Digits Took Six Years to Build

Before diving into the technology, Rob shared the origin story. Digits founder Jeff Seibert sold his previous companies to Box and Twitter. At both companies, he noticed a stark contrast: product and engineering teams had real-time dashboards showing exactly who was on their website and what buttons they clicked. But when he wanted to check if he had a budget for a team event, finance told him to wait 45 days for the books to close.

“As a founder of a company, you’re like, ‘This is crazy. I’m just going to do this event without your approval,’” Rob explained. When Jeff left Twitter, he wanted to use machine learning for good, and accounting emerged as the perfect candidate.

The result took six years to build. “Turns out that it takes a while to build a general ledger from the ground up in the machine learning era,” Rob admitted. But that ground-up approach makes all the difference.

The Three-Layer Intelligence That Replaces Bank Rules

Traditional accounting software makes you act like a programmer. You write rules, define patterns, and hope the software follows instructions. Anyone who’s debugged bank rules knows the frustration.

Digits flips this completely. Instead of you teaching the software through rules, the system learns from your work at three levels.

First, it learns from each specific company. When you connect QuickBooks to Digits, it imports your historical data and trains on how you categorize that company’s transactions. “We actually train on a company level,” Rob explained. “When transactions start coming in, it actually leverages the work you’ve already done within that individual company.”

Second, it learns from your entire firm. When a new vendor appears—say, a coffee shop that just opened—Digits checks if any other client in your firm has seen that vendor. Your work for one client helps all your clients.

Third, it taps into global intelligence. For truly novel transactions, Digits uses its global model trained on every transaction the platform has ever processed.

The payoff is significant. “For September, we’re at a 96% rate of transactions getting booked into Digits that then were subsequently not touched by a human afterwards,” Rob revealed. That’s not just categorized; that’s categorized correctly enough that accountants didn’t change them.

“You’re not editing any rules,” Rob points out, contrasting Digits with traditional systems. “You don’t have to add an extra appendage to pull out the specific Costco transaction. We learn from your behaviors directly inside the product.”

How the System Handles the Other 4%

No AI system is perfect. What matters is how it handles uncertainty. When Digits encounters a transaction it’s unsure about, it doesn’t guess silently. It flags the uncertainty and shows its reasoning.

During the demo, Rob showed a US Patent and Trademark Office transaction where Digits displayed, “I have this as taxes, but I actually think it could be legal.” The system even suggested adding “intangibles” as a new account category for companies still building their chart of accounts.

The learning happens instantly. “We’ve built our architecture to be uniquely quick in the training,” Rob emphasized. “The second we see a similar transaction, it’ll effectively be perfect based on your prior action.”

Quality control is proactive rather than reactive. Each month, Digits flags all new vendors so accountants can verify they’re categorized correctly. It also highlights vendors booked to multiple categories, like Apple transactions split between fixed assets and software subscriptions.

When accountants don’t know what something is, they can ask clients directly within the platform. Questions attach to specific transactions, clients get email notifications, and responses flow back to the same transaction. The AI suggests categorization based on the client’s answer, though accountants confirm before applying.

David appreciated the unified workflow. “Now I don’t have to have five browser tabs open where one browser tab is the report, the transaction is in a new browser tab, and I make the edit and refresh the report in the other browser tab.”

Reconciliation in Minutes, Not Hours

Bank reconciliation should be simple, but when something doesn’t match, like $15 missing from Stripe, the detective work begins. Digits transforms this process entirely.

Statements enter the system three ways. Banks like Wells Fargo send them automatically via API. For others, accountants drag and drop PDFs directly onto the platform. Every Digits account also gets a single email address that accepts any document type, including statements, bills, or receipts, The AI routes them appropriately.

“So one email for all the transactions in a client’s company file,” David noted. “You don’t have special HR email and AP email where you send it to the wrong box and it creates a mess.”

The reconciliation interface shows the bank statement PDF alongside ledger transactions. As you hover over transactions, green boxes highlight the matching line on the statement. David’s reaction captured what every accountant will recognize: “I used to do this with a highlighter and my fingers. I had to find it on both.”

But the real magic is proactive problem detection. Digits identifies specific issues and offers one-click fixes for things like:

Uncleared transactions that should move to next month
Statement items missing from the ledger
Date discrepancies between records and statements

Each issue comes with a resolution button. The system does the detective work; accountants just confirm the fix.

“We had an accountant come in the other day. He was like, ‘I did four years of cleanup in four hours’ because he just linked the bank accounts, dragged all the statements in, and the AI did everything,” Rob says.

Beyond Bookkeeping: Reports Clients Actually Read

With traditional financial reports, only 15% of business owners even open those black-and-white PDF attachments. Digits studied this and found that when firms use visual reporting tools, over 70% of clients actually open and interact with the financials.

The reporting system works like “Google Docs for your finances,” as Rob described it. Accountants can add commentary directly on line items, tag clients with questions, and create visual dashboards that tell the story of the business.

The platform includes built-in bill pay ($0.50 for ACH, $2 for checks) and invoicing. The system automatically recognizes and routes dragged-in documents. Bills queue for payment, receipts match to transactions, and statements trigger reconciliation.

Behind the scenes, AI agents continuously research every vendor, building what Rob called “a dossier” with logos, phone numbers, and descriptions. “This is what your team does when they don’t know what a transaction is. They Google it and find the information.”

What This Means for Your Practice

The shift from rule-based to AI-native software fundamentally changes the accountant’s daily work. Instead of programming rules, you review AI suggestions. Instead of hunting for reconciliation errors, you confirm one-click fixes. Instead of sending reports that get ignored, you create interactive dashboards that clients actually use.

The compound effect is striking. Every correction teaches the system, improving accuracy for that client, your entire firm, and eventually all Digits users. Time savings stack up, allowing firms to shift toward advisory work.

Digits offers a partner program with volume discounts. The standard price is $100 per month per client for full features, with special pricing for tax write-up work. Accounting firms get their own firm account free when joining the partner program.

Rob emphasized that construction and other complex industries might see slightly lower accuracy rates than the 96% average, but the system continuously learns and improves. Features like sales tax support and project tracking are coming soon, while departments and locations tracking are already available.

For firms evaluating new software, the question has shifted from “What rules do I need to create?” to “How well does this system learn?” The four-years-in-four-hours cleanup example shows what’s possible when AI handles the tedious work.

Watch the complete Earmark Expo webinar to see the full demonstration, including reconciliation workflows, client communication tools, and the visual reporting system that gets clients actually engaging with their financials. Whether you’re ready to switch or just want to understand where accounting technology is heading, this demo shows what accounting looks like when bank rules become obsolete.

The Accounting Platform That Achieves 96.5% Automation Reveals How They Did It

Earmark Team · December 22, 2025 ·

“No one’s going to be outcompeted by the AI itself. You are going to be outcompeted by firms that really adopt this aggressively,” warns Jeff Seibert, whose company just hit 96.5% accuracy in automated bookkeeping—something that seemed impossible just a few years ago.

In this milestone 100th episode of the Earmark Podcast, Blake Oliver sits down with Jeff Seibert, co-founder and CEO of Digits, to explore how AI is fundamentally changing the architecture of accounting software. Seibert brings fresh eyes to accounting—he previously led consumer product at Twitter and built Crashlytics (now running on six billion smartphones). His frustration was simple: Why could product teams access real-time analytics while business owners waited weeks for black-and-white spreadsheets?

Founded in 2018, Digits set out to reimagine accounting in the age of machine learning. While traditional software treats transactions as meaningless text in rigid databases, Digits achieves near-perfect automation by treating financial data as interconnected objects that learn from patterns across millions of transactions.

The 30-Year-Old Problem Holding Back Accounting

As Seibert sees it, the fundamental issue facing bookkeeping automation is that every major accounting platform—QuickBooks, Xero, and even NetSuite—runs on relational databases designed 20-30 years ago. These systems treat transactions as simple text entries with no understanding of what they mean.

“QuickBooks is just going to see an Uber transaction as “U-b-e-r”. It just sees text,” Seibert explains. “It doesn’t understand the data, it doesn’t know what Uber actually is.”

This limitation explains why Intuit, with all its resources, has yet to deliver meaningful automation. The answer is architectural. Each QuickBooks company exists in its own isolated database, preventing the software from learning patterns across businesses. The constraints run so deep that QuickBooks still can’t handle having a vendor and customer with the same name—it appears they chose “name” as the primary database key decades ago.

Digits takes a completely different approach using what’s called a vector graph data model. Everything becomes an object—Uber is an object, your expense categories are objects, your bank accounts are objects. Transactions become connections between these objects, creating a web of financial relationships the AI can understand.

This mirrors how large language models (LLMs) work, converting transactions into vector embeddings, essentially plotting them in multi-dimensional space where similar items cluster together. When trained on 170 million transactions representing nearly $1 trillion in business activity, patterns emerge that would be obvious to humans but invisible to traditional software.

“When you have that scale of data and you see how everyone has booked Uber before, you start to see patterns,” Seibert notes. “The model starts learning. If it sees Lyft in your accounting for this client, it then knows how to book Uber.”

How AI Agents Actually Work (Hint: Like Clever Interns)

The accounting world is buzzing about “AI agents,” but what are they really? Seibert explains, “An agent is simply an LLM that you run in a loop. You give it a task, it attempts the task, you ask if it completed it. If not, it continues until it’s done.”

Think of them as clever interns who never get tired. Digits has been running these agents in production since January 2024, primarily for researching unfamiliar transactions.

The system uses three layers of intelligence. First, it checks if this specific client has seen this transaction before. If yes, it books the transaction exactly the same way. Second, if the transaction is new to this client but familiar to the platform, it uses its global model trained across all users. Third, for completely novel transactions, the agent literally Googles them.

“What would you do as an accountant? You would probably Google it,” Seibert explains. “What do our agents do? They literally Google it, research the transaction, build a dossier about it.”

As a result, only 4-5% of transactions now require human review, compared to the 20% that typically slip through even well-maintained rule-based systems. Notably, the system maintains strict confidence thresholds. Any transaction it is unsure about gets flagged for human review. It never guesses when uncertain.

The upcoming reconciliation feature shows how sophisticated these agents have become. The system pulls statements directly from banks or extracts them from PDFs, then matches transactions with pixel-level precision. “You can literally click on the transaction and see it on the statement and vice versa,” Seibert says. This builds trust with accountants who need to see exactly where the numbers come from.

What This Means for Your Firm’s Future

As of August, Digits hit 96.5% accuracy, up from 93.5% in spring. Each percentage point represents thousands of transactions that no longer need human touch. But it begs the question: how do you price services when the work happens automatically?

“If you’re charging purely per hour right now, then automation may make that challenging,” Seibert acknowledges. But forward-thinking firms are already adapting. They’re moving to fixed-fee models for routine work like monthly closes, which become increasingly profitable as automation reduces time investment. Many use a hybrid approach, charging fixed fees for the close, and hourly rates for advisory work.

At a flat $100 per month (with volume discounts for accounting partners), Digits offers predictable pricing that contrasts sharply with QuickBooks’ constant increases. The platform even offers specialized SKUs for ledger-only or reporting-only clients, accommodating diverse practice needs.

The staffing implications are real but not apocalyptic. Junior bookkeeping roles focused on data entry will diminish. But Seibert points out this could make the profession more attractive: “You don’t want to just sit there doing data entry all day long. You want to learn how to advise businesses.”

Seibert recommends firms start small when implementing automated bookkeeping. “Pick one client in your firm and see what you can achieve,” Seibert challenges. Choose a simple, digital-native business like consultants, SaaS companies, or agencies with predictable electronic expenses. Build confidence, then expand to complex cases.

Building Trust Through Transparency

With financial data flowing through AI systems, security is crucial. Digits addresses this with architecture developed at Seibert’s previous companies, where they handled crash data from billions of smartphones.

Everything stays within Digits’ systems; they don’t send raw data to OpenAI or other third parties. All data is encrypted at rest using per-object envelope encryption, where each object has its own encryption key. Even if breached, stealing one key wouldn’t compromise the system.

The platform is SOC 2 Type 2 certified, with complete audit trails showing who changed what and when. You can even grant granular access, like giving your marketing manager visibility into only marketing expenses. “They can see marketing, all the transactions booked to marketing, and nothing else,” Seibert explains.

Importantly, when AI does the work, you can trace exactly what happened. Click on any transaction to see the activity log. This solves the common problem of clients making changes in QuickBooks without anyone knowing.

The Competitive Reality Check

Seibert’s warning deserves repeating: “No one’s going to be outcompeted by the AI itself. You are going to be outcompeted by firms that really adopt this aggressively.”

This isn’t hypothetical. Firms using advanced automation already serve more clients with similar-size teams, offer competitive pricing while maintaining margins, and provide real-time insights that clients increasingly expect.

You don’t have to become a tech expert. Set aside time each month after the close to try new tools. Watch YouTube videos about AI agents (though Oliver warns to avoid the hype channels). Most importantly, maintain healthy skepticism. As Seibert notes about AI doing math, “If it’s not 100% correct, what’s the point?”

Remember, AI agents are like clever interns. They’re eager, overconfident, and need supervision. They excel at tedious, repetitive tasks but need human judgment for nuanced decisions. The goal isn’t to replace accountants but to eliminate the work accountants wish they didn’t have to do.

Taking the First Step

Thoughtfully evaluate how these innovations can augment your practice. Start with one simple client. See what 96.5% automation actually feels like. Build confidence, then expand gradually.

Listen to the full episode to hear Seibert’s complete vision and practical guidance on everything from selecting pilot clients to restructuring pricing models. The tools to eliminate tedium while amplifying expertise aren’t coming; they’re here, proven, and improving rapidly. How quickly and thoughtfully can you integrate it?