INFO
From a two-year backlog to instant go-live A publicly listed restaurant chain with stores across the country gets requests from frontline teams in every region. Its product and engineering teams couldn't say no to the flood of requirements, and the backlog jammed up two years out. With Lovrabet, operations staff simply describe what they need and the AI generates the business system — which went live 6 months ahead of the original plan.
| Industry | Scenario | Delivery model |
|---|---|---|
| Restaurant chain | Self-service business system generation | AI-Native |
A backlog stretched two years out
This chain runs hundreds of stores across the country. Every region has its own needs — new promotion pages, regional specialty-menu management, localized membership programs — and the requests never stop.
Facing this flood, the product and engineering teams could neither reject it all nor respond quickly. The schedule kept growing until the furthest-out work sat two years away, while frontline staff watched market opportunities slip by.
An IT bottleneck holding the business back
- Hundreds of stores, requests from every region nationwide — huge in volume and highly varied.
- The product and engineering teams couldn't say no, and the backlog jammed two years out.
- Business teams wanted to launch systems themselves, without waiting for IT.
- Under traditional development, response speed couldn't keep up with the market.
Self-service development for the business
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Text-to-system generation Operations staff describe the requirement in natural language, and the AI generates a complete business system.
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Built-in common capabilities Data management, export, multi-table joins, and other common features are built in — no engineering slot needed.
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Fast validation and iteration From description to live system, the whole path is self-service, closing the loop between business and product.
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An engineering backstop Complex requirements still go to the engineering team for extension, forming a model of business self-service plus an engineering backstop.
Project outcomes
| Metric | What it measures | Details |
|---|---|---|
| 6 months | Early go-live | Ahead of the original plan |
| Instant | Response speed | From two years to immediate |
| Resolved | IT bottleneck | Business teams develop systems themselves |
| 50%+ | Shorter cycle | Delivery efficiency significantly improved |
Key insights
- Business people become the primary driver of delivery: whoever raises a requirement also drives it to done, and business intent no longer sits stuck in a backlog.
- 80% of standard requirements can be self-served by business teams, letting engineering focus on the 20% that is core innovation.
- Validate fast, iterate fast — the product serves the business instead of the business waiting years for the product.
- An IT bottleneck isn't unsolvable. With an AI-Native model, technical capability can empower the business at scale.
Takeaways for similar enterprises
If your company also faces too many requests, a backlog that's too long, and a business that can't wait, Lovrabet's self-service development model can be the key to breaking the IT bottleneck.
The point isn't making IT run faster — it's letting the business run on its own: from "file a request → wait for IT → accept" to "build directly → iterate immediately".
INFO
The core message for customers Lovrabet's value isn't rebuilding your system from scratch — it's building on your existing data, processes, and business semantics so AI can understand them, act on them, and capture the know-how as reusable capabilities.