INFO
How Lovrabet helped a designated-driving SaaS company upgrade its delivery model A SaaS company that has long served designated-driving platforms used to re-map rules, rebuild reports, rewrite integrations, and retrain operations staff for every new customer. Lovrabet helped them capture their know-how — late-night peak dispatch, exception-order handling, complaint liability determination — into custom SQL, BFFs, and Skills. What they deliver is no longer just system pages, but a replicable digital employee for designated-driving operations.
| Industry | Scenario | Delivery model |
|---|---|---|
| Designated-driving SaaS | Late-night peak operations dispatch | AI-Native |
Delivery and operations are slowing this designated-driving SaaS company's growth
This SaaS company has served regional designated-driving platforms and mobility businesses for years, building mature product capabilities in order dispatch, driver management, pricing configuration, customer-service tickets, complaint handling, and business analytics — and it knows the industry and its customers deeply.
But customer rules kept fragmenting and expectations for AI-driven operations kept rising. Sticking to pages, forms, and reports kept dragging them into a project-style loop: thin margins, heavy implementation, and little reuse.
Designated-driving customers don't lack systems — they lack replicable operations capability
- Every customer's city operations rules, driver hierarchy, subsidy strategy, complaint workflow, and channel integrations differ, so implementation teams keep re-learning and re-building the same things.
- High-frequency, high-value scenarios — late-night peak dispatch, exception-order handling, complaint liability determination — stay scattered across reports, scripts, back-office configuration, and people's heads.
- More projects bring more implementation and engineering pressure, yet the industry know-how that really matters never becomes a reusable asset for the next customer.
- Customers started asking whether AI could analyze supply and demand automatically, handle exceptions, and reuse their best operating practices — questions traditional SaaS delivery struggles to answer.
Co-creating project experience into a designated-driving operations digital employee
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Understand customer legacy systems with DBAgent Reverse-engineer business objects from existing data structures — orders, drivers, cities, business districts, pricing, subsidies, complaints, and settlements — instead of mapping each customer's legacy system from scratch.
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Standardize definitions with custom SQL Capture key queries — driver shortfall, order backlog, wait time, cancellation rate — as unified sqlCode entries, so Web, Agent, Skill, and BFF share one set of business definitions.
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Wrap critical actions in Backend Functions / BFFs Package high-stakes actions — subsidy delivery, driver notifications, exception review, ticket creation, audit write-backs — into stable services that stay controllable, permissioned, and traceable.
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Capture operating SOPs as Skills Write late-night peak dispatch as a business-process markdown in natural language, then let business teams orchestrate data capabilities and business actions through the Skill — a replicable digital employee template.
Project outcomes
| Metric | What it measures | Details |
|---|---|---|
| Weeks → template launch | Delivery kickoff | From rebuilding for every customer to industry template + semantic understanding + asset adaptation |
| SQL/BFF/Skill | Core assets | Query capability, business actions, and operating SOPs consolidated into reusable capability entry points |
| Digital employee | Product form | From a dispatch back office to a designated-driving operations package that analyzes, executes, and reviews |
| Subscription + managed service | Revenue structure | From one-off delivery to template licensing, continuous optimization, and managed operations as long-term services |
What this case shows
- What's genuinely scarce isn't a few more back-office pages — it's turning dispatch, subsidy, complaint, and customer-service experience into industry capabilities that can be called again and again.
- AI-Native delivery doesn't rebuild a software company's product for them; it helps them turn industry know-how, customer scenarios, and implementation experience into platform assets and products.
- A Skill is best understood as a business-process markdown: the business team organizes the judgment calls and the order of collaboration, while the engineering team maintains the underlying SQL and BFFs.
- Once query definitions, critical actions, and SOPs become standardized assets, a software company sells more than a system — it sells a sustainably operable industry digital employee.
Takeaways for ISVs and traditional software companies
The more realistic path isn't building an all-encompassing new platform from day one. Start with one high-frequency, high-pain, easily-closed business process and polish it into a digital employee template you can demo, deliver, and replicate.
When software companies turn industry experience into products, project delivery into a platform, and one-off development into recurring service revenue, customers are no longer buying pages — they're buying business capabilities that keep compounding.
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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.