We Should Be an AI-Native Company First

We often describe SnapLedger as AI-native SaaS.

But recently I have been thinking about what that should actually mean.

It cannot simply mean that we put an AI assistant inside accounting software.

If we want to build software for AI-native businesses, SnapLedger itself should first operate like an AI-native company.

And increasingly, that is what we are trying to do.

Take customer acquisition.

With Snappy Front Desk, we can start with a simple idea about the kind of customer we want to reach.

Snappy can help turn that idea into a real targeting strategy, search for qualified prospects, evaluate the results, and improve the strategy until it becomes an executable cold campaign.

Then it can start doing the work.

Through email and WhatsApp, Snappy can reach potential customers, introduce SnapLedger, answer questions, continue conversations and help interested customers move toward subscription.

But the job doesn't end when someone signs up.

Once a customer enters SnapLedger, Snappy can observe how the product is actually being used.

Where did the customer stop?

Which important feature haven't they discovered?

Are they confused during onboarding?

Do they need help completing the next step?

Instead of waiting for a customer to read a manual or contact support, Snappy can increasingly provide guidance while the customer is actually using the product.

So the same AI system participates in the journey from:

Finding a customer → talking to them → converting them → onboarding them → helping them succeed.

And then another loop begins.

Customers can simply tell Snappy what they want.

They can suggest a feature.

They can report something confusing.

They can report a bug.

Behind the scenes, these conversations can become structured engineering tickets. AI agents can investigate the issue, propose changes, implement fixes and run tests.

I don't have to personally write every line of code.

But this doesn't mean removing humans from the loop.

I regularly review these changes, understand what was changed and why, and make sure the code entering SnapLedger meets the quality I expect.

So our product development loop is increasingly becoming:

Customer → Snappy → Ticket → AI Engineering → Test → Founder Review → Product

The distance between a customer's problem and a product improvement becomes much shorter.

But there is another part of being AI-native that I think is equally important.

Daoile, the company behind SnapLedger, is itself a SnapLedger customer.

We manage our own accounting in SnapLedger.

We manage our compliance in SnapLedger.

We upload our own documents.

We reconcile our own transactions.

And increasingly, instead of operating accounting software screen by screen, we simply talk to Snappy about what needs to be done.

This matters to me.

Because every time something is inconvenient for us, we experience the same inconvenience our customers experience.

Every time Snappy doesn't understand something correctly, we see it ourselves.

And every time we make SnapLedger easier for Daoile to operate, there is a good chance we are making it easier for another small business too.

This creates another loop:

Build → Use → Discover → Improve → Use Again.

Perhaps this is how I increasingly define an AI-native SaaS company.

It is not a SaaS company that happens to use AI.

AI becomes part of how the company finds customers, serves customers, learns from customers, builds its product, and runs itself.

And humans increasingly move toward the places where they add the most value:

setting direction, understanding customers, making judgments, and maintaining quality.

We want SnapLedger to help small businesses become AI-native.

The best place to start is with ourselves.