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How AI-Native infrastructure reshaped an insurance product onboarding process A leading insurance brokerage represents products from 200+ insurers and onboards a hundred-plus products every month. Each product used to involve dozens of rules scattered across PDFs, Excel files, and paper documents; product managers and the engineering team went back and forth to align, and onboarding averaged 20 working days. With Lovrabet, product managers write the insurance rules in Markdown, and the AI parses the business semantics, generates execution instructions, and writes them into the data tables. Core configuration is down to 30 minutes, and the full onboarding cycle to about 2 days.
| Industry | Scenario | Delivery model |
|---|---|---|
| Insurance brokerage | Insurance product onboarding and rule configuration | AI-Native |
The pain of listing new products in insurance brokerage
Every month this company onboards a hundred-plus insurance products, each carrying dozens of rules — eligibility restrictions, age limits, regional requirements, discount plans, waiting-period clauses. The source material comes from different insurers as PDFs, Excel files, and paper documents, so gathering, checking, and entering it is enormously labor-intensive.
More importantly, the brokerage system that runs the business has been in production for over 12 years and carries deep historical data and complex business relationships. A ground-up rebuild would be high-risk and expensive, so the IT team won't touch it lightly — while the business stays locked in a slow onboarding process.
Product teams stuck in the waiting chain
- Dozens of rules per product — eligibility restrictions, age limits, regional requirements, discount plans, waiting periods — with source information scattered across all kinds of documents.
- Product managers first performed 200–400 information-gathering operations, then handed off to the engineering team for 300–500 back-office page operations.
- Constant translation between product language and system language: one product often took 8–15 rounds of discussion, correction, and verification.
- A 12-year-old system heavy with historical data and complex relationships — too costly and risky to rebuild, so the IT team could only maintain it carefully.
Let product managers talk to the system directly
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Markdown spec documents In a standard Markdown format, product managers describe insuring age, eligible populations, regional restrictions, waiting periods, discount plans, and family-policy definitions — in the insurance language they already use.
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AI parses the business semantics Lovrabet recognizes terms like policyholder, insured, primary coverage, rider, and waiting period, and understands the business meaning behind complex insurance rules.
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Generate instructions and write them into data tables Following insurance-industry SOPs, the AI generates data-operation instructions, calls system APIs to complete the writes, and automatically produces validation logic for age, identity, time, and more.
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Human review, then publish Product managers only do the final acceptance check and release — no more waiting for an engineering slot or re-litigating requirement details.
Project outcomes
| Metric | What it measures | Details |
|---|---|---|
| 20 days → 2 days | Onboarding cycle | From 20 working days on average down to about 2.5 working days |
| 30 minutes | Core configuration | AI parsing and automatic writes complete the core setup |
| 96% | Efficiency gain | Measured at roughly 96% efficiency improvement per product |
| 8–15 rounds → acceptance | How work gets done | Product managers moved from waiting on engineering to executing on their own |
What this case shows
- Slow insurance product onboarding isn't simply a staffing shortage — it's the missing bridge, one AI can understand and act on, between business rules, system configuration, and validation logic.
- For systems that have run for years, value doesn't have to come from tearing everything down. Let AI read the business semantics first, then drive the existing system in a controlled way.
- When product managers can drive the system directly in the business language they know, digitalization shifts from "file a request and wait for a slot" to "the business executes on its own".
- The engineering team moves from repeat typist to supervisor and enabler, freeing its energy for system governance and capability building.
Takeaways for financial and insurance enterprises
If your core system has been running for years, is deeply interrelated, and is risky to rebuild, you don't have to tear it down first. The more realistic path is to keep the existing system running stably while introducing an AI execution layer that understands business semantics.
Lovrabet's AI-Native enterprise runtime infrastructure turns business language into executable operations. Business people take charge of onboarding, configuration, and validation, and digitalization moves from "people chasing systems" to "business semantics driving system execution directly".
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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.