We Rewrote Our Stack with AI in 24 Hours (Saved $500k/year)

Here's a number that should make every CFO sweat: $500,000 per year.

That's what one company was paying for a JSONata license. A query language for JSON. Not a platform. Not an operating system. A single tool for querying JSON data.

Then they rewrote it with AI in a single day.

"We honestly didn't believe it would work. We scheduled a full sprint. The AI finished before lunch."

The Setup

JSONata is a powerful JSON query language. Think SQL for JSON. It's used everywhere — APIs, data pipelines, ETL workflows. The enterprise license? Expensive. Worth it, maybe, if there were no alternatives.

But now there's Claude, GPT-4, and a new breed of AI that doesn't just write code — it understands systems.

The Rewrite

Here's what the team did:

1. Documented the use cases. They listed every JSONata query in production — about 200 queries across 12 services.
2. Generated the replacement. They fed the JSONata spec and their query patterns into Claude. Output: a pure JavaScript/TypeScript implementation.
3. Tested exhaustively. Every query. Every edge case. The AI-generated code passed 100% of their existing test suite.
4. Deployed. One day later, they were live with their own implementation.

Why This Matters

This isn't just about JSONata. This is about the entire enterprise software model.

For decades, companies have paid premiums for specialized tools because building alternatives was too expensive. That calculus just changed. The cost of building dropped from months of engineering time to... hours.

The New Equation

Old model:

License cost: $500k/year
Build cost: $2M+ (engineering time, maintenance, risk)
Decision: Buy license

New model:

License cost: $500k/year
Build cost: $50k (AI-assisted development, one-time)
Decision: Build. Immediately.

What's Rewrite-able?

Not everything. But more than you think:

• Query languages (JSONata, GraphQL variants, DSLs)
• Middleware (API gateways, auth layers, rate limiters)
• Internal tools (dashboards, admin panels, reporting)
• Data transformations (ETL scripts, parsers, converters)
• SDKs and clients (API wrappers, database drivers)

The pattern: anything that's "just code" with well-defined inputs and outputs is now a candidate for AI rewrite.

The Catch

There are risks:

• Maintenance. You own it now. Bugs are your problem.
• Edge cases. AI is great at 95%. The last 5% can be brutal.
• Security. Generated code needs the same scrutiny as human code.
• Legal. Check your licenses. Some prohibit reverse-engineering.

But here's the thing: the teams that figure this out first get a massive cost advantage. The teams that don't? They're subsidizing their competitors' R&D.

What I'd Do

If I were running an engineering org today, I'd:

1. Audit the stack. List every tool with a license over $50k/year.
2. Assess rewrite potential. Can AI generate a replacement? (Check API specs, query patterns, use cases.)
3. Run a pilot. Pick one non-critical tool. Give an engineer a week. See what happens.
4. Scale what works. The savings compound fast.

The Bottom Line

$500k saved. One day of work. Zero ongoing license fees.

This story is going to repeat across enterprise software. The only question is whether you're the one rewriting, or the one paying the license.

The math has changed. Act accordingly.