Cookie Finance's
AI Assisted Ledger
COOKIE FINANCE — MAY 2026
My Role
Lead UX/Product Designer — Research, User Flows, Visual Design, Prototyping, QA
Tools
Figma, Figjam, VS Code, Jira, Github, Claude Code, Codex
Team
1 UX Designer
4 Developers
Management — CTO, CEO, VP of Product
Duration & Status
3 Months, Shipped in May 2026
Industry
Internal tools, Fintech, AI, Complex Workflows
Context
Cookie Finance provides bookkeeping and tax services for content creators, and each bookkeeper managed 100 to 200+ clients with thousands of transactions each. Their QuickBooks workflow was slow and often broke, so leadership wanted an in-house ledger powered by AI, shipped in 3 months. The catch: bookkeepers didn't trust AI and would rather do the work by hand.
How I added value
As the lead designer, I turned leadership's push for AI into a ledger bookkeepers would actually use. I designed confidence-based AI routing and AI vs. human icons so bookkeepers could always check AI's work, designed around technology constraints, and stayed on through production, leading QA and pushing fixes myself.
0.1
COOKIE AI LEDGER WALKTHROUGH
VIDEO
0.2
transaction history recording
VIDEO
0.3
Recategorizing in Bulk
VIDEO
Bookkeepers were buried in transactions
Each bookkeeper managed 100 to 200+ clients, and each client could have 1,000 to 10,000+ transactions. Multiply that across a portfolio, and bookkeepers were practically buried (Figure 1.0).
QuickBooks was slowing them down
Most of their work happened in QuickBooks, but it often made the job harder. It could take minutes to load, disconnect bank accounts, and force bookkeepers to redo work they'd already finished (Figure 1.1).
Slow cleanup held up everything else
Every new client starts with a historical cleanup of their past transactions, and nothing else can start until it's done: not monthly bookkeeping, payroll, LLC setup, or taxes (Figure 1.2). As we scaled, cleanups that should take weeks could stretch into months, leaving clients paying for services they couldn't use yet.
Build our own ledger, with AI inside
Leadership saw a way out: replace QuickBooks with our own ledger and use AI to categorize transactions. If AI could handle even 10% of the work, cleanups would move faster and each bookkeeper could take on more clients.
2.0
MESSAGE FROM CEO
VIDEO
A pile up of constraints...
It was a smart bet, but a big one. The ledger is where bookkeepers do their work every day, and we had to build it within two major constraints:
Bookkeepers didn't trust AI
On top of those constraints, the people we were building for didn't trust what we were building. In my interviews, 4 out of 5 bookkeepers said they'd rather do the work by hand than redo AI's work. Their biggest fear: if AI got it wrong and they couldn't catch it, it would mean even more work (Figure 3.0).
And they had good reason
Categorization isn't as simple as it looks. Many transactions come in ambiguous, so bookkeepers categorized them using their own research and knowledge of each client. That was context AI couldn't easily see (Figure 3.1).
My guiding principles
What I learned shaped two principles that guided every decision:
Mapping how a transaction moves today
Before placing AI anywhere, I mapped how a transaction moves through cleanup today. Bookkeepers clean up a client's past transactions, and anything uncertain goes to Ask My Client (AMC) for more context. It then gets recategorized and finally lands in Categorized (Figure 4.0).
Placing AI where people can check it
I placed AI inside that same flow based on confidence, so it never skips a checkpoint. Low-confidence transactions surface at the start of cleanup, so bookkeepers catch them early. High-confidence ones (70%+) go straight to Categorized, where they can still be reviewed and corrected (Figure 4.1).
A layout built for thousands of rows
I explored two layouts, and bookkeepers pushed back on the first. Layout B gave them far more vertical space, which matters when scrolling through thousands of transactions (Figures 5.0 and 5.1).
Tabs that mirror the workflow
I turned the cleanup flow into tabs, so bookkeepers always know where a transaction stands and how far along they are in each client's cleanup (Figure 5.2).
Showing who touched what
I first proposed a hover state explaining why the AI chose a category, like showing your work in math class (Figure 5.3). Engineering couldn't support AI reasoning yet, so I scoped it down to icons marking whether AI or a person categorized each transaction, and kept reasoning as a future goal (Figure 5.4).
An audit trail on every transaction
Bookkeepers' biggest request was a clear history. Clicking into any transaction shows who categorized it and when, keeping both AI and teammates accountable (Figure 5.5).
Bulk actions for bulk volume
One-by-one editing doesn't work with thousands of transactions. I designed bulk tools to confirm, recategorize, and reassign vendors (Figure 5.6), and added bulk splitting, a heavily requested feature that didn't exist before (Figure 5.7).
Bringing Ask My Client into the ledger
QuickBooks had no AMC feature, so bookkeepers copied transactions into spreadsheets and emailed clients, and transactions often got lost (Figure 5.8). I built the AMC lifecycle into the ledger, with Creator Accountants owning client communication (Figure 5.9).
6.0
AI LEDGER WALKTHROUGH
VIDEO
6.1
transaction history recording
VIDEO
6.2
Bulk recategorization Recording
VIDEO
