From an existing database to an AI-Native business system
INFO
Lovrabet's value isn't a chat box bolted onto your legacy systems. It starts from the databases, legacy systems, APIs, processes, and know-how your enterprise already has and quickly modernizes them into an AI-Native system foundation. Business users don't have to learn development or configuration first — they can raise requests, query data, analyze problems, and drive tasks directly through the Agent.
What you get
| The old way of using systems | With Lovrabet |
|---|---|
| People hop between multiple systems to look up data, fill in forms, cross-check, and route work | People state the goal; the Agent queries, analyzes, executes, and delivers results |
| The database is just tables, fields, and interfaces | Data is organized into business objects, metric definitions, permission boundaries, and executable actions |
| Expertise lives with veteran employees and project teams | Proven paths are captured as Skills, SOPs, knowledge, and reusable processes |
| Pages are the main entry point | The Agent becomes the entry point for running the business; pages carry high-frequency, stable results |
How Lovrabet modernizes existing systems
Lovrabet doesn't ask you to tear everything down and rebuild. It starts from your existing assets, extracts the business meaning behind your legacy databases and systems, and puts it to work for the Agent.
| Stage | What Lovrabet does | Business value |
|---|---|---|
| Connect legacy assets | Links databases, legacy systems, APIs, BFFs, SQL, and process documents | Activates existing assets instead of building from zero |
| Understand business semantics | Identifies objects, relationships, metrics, and permissions through DBAgent and business modeling | The AI doesn't just get the data — it understands what business the data represents |
| Package execution capabilities | Turns SQL, Backend Functions, APIs, pages, and Skills into callable assets | The Agent can query, analyze, and execute — not just answer |
| Go live | Business users query data, analyze, and handle tasks through the Agent | Business users see results first, without entering development mode |
| Capture and reuse | Turns proven paths, corrections, and judgment criteria into knowledge and Skills | The next similar task runs faster, more accurately, and more consistently |
How business users get work done with the Agent
| What I want to do | How the Agent helps | The result |
|---|---|---|
| Look up a piece of business data | Pulls data by business object and metric definition — no need to know table or field names | An explainable data result |
| Analyze a business problem | Combines metrics, history, and context into evidence for judgment | Root cause, impact scope, and next-step recommendations |
| Push a business task forward | Calls SQL, APIs, Backend Functions, or Skills to take action | Records created, stakeholders notified, statuses updated |
| Reuse a successful approach | Saves the working path as a Skill or knowledge entry | The team can reuse it directly next time |
TIP
The core shift: people used to drive the systems; now AI drives the process, while people state requirements, confirm critical gates, and judge exceptions.
How the Agent and pages work together
The Agent and pages aren't an either/or choice.
| Use case | Recommended entry |
|---|---|
| Ad-hoc queries, exploratory analysis, cross-system tasks, one-off processing | Agent |
| High-frequency, stable processes; fixed menus; lists, forms, approvals, reports | Pages |
| Explore first, then standardize | Run it through the Agent first, then capture it as a Skill, a page, or a standard process |
TIP
A more precise way to put it: Lovrabet forms an AI-Native business foundation — the Agent handles flexible execution, pages carry stable results, and Skills and knowledge drive continuous reuse.
Next steps
If you're a business user, pick one real problem and let the Agent handle a data question, an analysis, or a task for you.
If you're on the technical or implementation side, start by connecting your existing databases and legacy systems, then fill in the business models, permissions, SQL, Backend Functions, APIs, and Skills.