One Month In:
What Our First Users Are Teaching Us

We launched aiSmartBudget to a small group of friends and family on May 4. Real people. Real bank accounts. Real goals.

Thirty days later, here is what we learned, what we shipped, and what is coming next.

The Feedback Changed How I See the Product

Survey responses are useful. Watching someone actually use the product is different. When a real user sits down with the app and works through their financial life, they show you things no survey would ever surface.

Two moments from this first month shaped where we are going next.

Bank Connectivity: Building in the Right Order

Before we wrote a single line of application code, we met with Plaid and talked through how bank connectivity would work. We made a deliberate choice: build the features that make bank data meaningful first. Connecting a bank account to a half-built product is wasted infrastructure.

Then one of our early users told us plainly that the process of fetching PDF statements from their bank website and uploading them was creating real friction. We had always planned to add Plaid. That feedback confirmed the timing was right.

One and a half days of engineering later, the integration was live for our entire Friends and Family group.

Before rolling it out, we tested the connection against personal account data at our own bank. The part I was most focused on was the category mapping: Plaid uses its own transaction categorization system, and we needed to map that accurately to aiSmartBudget’s category structure. Seventy-five category mappings. The matching function ran at 100% accuracy against our expectations, including a couple of transactions we honestly expected might not categorize at all. Those came through correctly flagged with no category rather than a wrong one.

Now transactions sync automatically. No more PDF downloads. No more manual uploads. Connect your bank and your history is there.

The strategic bet paid off. Building the intelligence layer before the connectivity layer meant Plaid had something real to connect to.

Goal Coaching: Seeing the Whole Picture

One of our early users is saving to buy a home. When he set up his goal in aiSmartBudget, the product showed him a target and a timeline. That is the beginning.

What he actually wanted to know was: can I get there, and what does it look like when I do?

That session showed us these pieces need to connect. We already built a home pre-qualification engine earlier this year. What we are building now ties the full journey together.

Near term: the AI Advisor will calculate the monthly contribution a user can realistically make toward a down payment, based on actual surplus cash flow rather than an estimate they type in. It will model the timeline across three savings vehicles: a standard savings account (around 0.1%), a high-yield savings account (around 4.5%), and a broad market index fund (around 10%), with guidance on which vehicles make sense at different time horizons. The product will not tell you what to do. It will show you what the numbers look like under each scenario.

Longer term: a complete picture of what owning the home actually costs month to month. Principal and interest, property taxes, homeowners insurance, private mortgage insurance with coaching on when PMI drops off as equity builds, and HOA dues where applicable. Shown side by side with what you pay in rent today. And because the product already knows your income from your financial profile, the AI Advisor will be able to tell you how close you are to qualifying at your target price.

The question is not just “when can I save enough for the down payment?” It is “what does my financial life look like when I get there?”

The Grocery Store Question

Another early user raised something simpler and just as important. Standing in a grocery store, mid-shop, the question was: how much of my monthly grocery budget have I used so far, and how much do I have left?

The second question was more pointed: based on my projected cash flow for the rest of the month, am I at risk of an NSF fee?

Both answers were in the app. Getting to them quickly enough to be useful in the moment was the gap.

We built a mobile version of aiSmartBudget that installs directly from your phone’s browser, no app store required. Two tabs. The Register tab shows your recent transactions and running balance, along with a projection of your account balance for the remainder of the month. If your cash flow is tracking toward an overdraft before it happens, you see the warning there. The Budget Health tab shows your flexible spending categories in real time: how much of your monthly budget you have used in each category and how much remains, with a green, yellow, or red signal so the answer is immediate.

Open it in the store. Three seconds to an answer.

What Is Next

This first month confirmed the strategic decisions we made early. It also showed us exactly where the product needs to grow.

New users need a clearer path from signup to their first real insight. We are building a guided setup experience that walks through connecting accounts, reviewing the AI-suggested financial rules, and setting a first goal. Each step grounded in the user’s own data, not a generic product demo.

The goal is not a tutorial. It is getting every new user to the moment where the product starts telling them something specific and true about their own financial picture, as quickly as possible. The deeper the AI Advisor insights, and the faster they surface, the more value a new user gets on day one.

We are planning for general release in July. If you want to be notified when aiSmartBudget opens to the public, sign up below.