AI-Ready Data System
Preparing scattered business data for AI-driven reporting
Key Results:
- Structured data from 50+ brands
- Secure AI access to data
- Data-driven decision-making
Your AI solution will be as effective as the setup you prepare beforehand. We structure the data AI needs, connect your systems through controlled access, and add the operational and security mechanisms your use case requires.
Connect an AI tool straight to an ERP or CRM, and any mistake it makes lands in live records. Forbytes puts APIs and MCP between the AI tools and your core systems. Each connection has a clear contract that states what it is allowed to do. Your agents work with business systems, and the contracts keep them from reaching further than that.
The answer AI gives depends on which system it reads, because the same customer or order can mean something different in each one. Forbytes pulls the required data together and normalizes it, then defines what each table and field means. The AI gets business data it can interpret correctly to provide accurate, data-driven answers.
Without operational control, AI can make changes that are hard to trace or undo. Forbytes adds audit trails, monitoring, and rollback mechanisms so the team can see what happened and recover when an action goes wrong. This gives you a way to track AI activity, reverse changes where possible, and require approval where they can’t be undone.
AI should not get more access than the person who asks it to do something. Forbytes applies user-level permissions, masks sensitive data, and routes high-impact actions for human approval. Your existing access rules stay in place while AI works with the systems behind them.
| Legacy Modernization Benefit | Impact on Your Business |
|---|---|
| Shared data structure across business systems | AI uses consistent business data, reducing manual reconciliation |
| Defined interfaces between AI and core systems | New AI apps integrate with existing software and speed up business answers |
| Clear limits on AI access | Sensitive data stays protected by access controls, reducing AI rollout risks |
| Monitoring and audit trails | Teams can see where an AI action came from and what happened afterward |
| Controlled approval points | AI does not make high-risk changes without a person checking them |
| Rollback mechanisms for critical operations | Mistakes can be corrected without undoing unrelated system activity |
AI-readiness discovery sessionExamining your existing systems and the business problems or questions your AI use case needs to solve.
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Fixed-price AI-readiness assessmentAssessing the project scope, ending with use case gaps, architecture, estimate, and a go/no-go recommendation.
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AI readiness implementationImplementing the required data layer, system connections, operational controls, and access rules.
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Use cases & business questionsExploring what the AI has to answer or do and which business systems it needs to access.
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Working AI-readiness pilotDesigning the AI setup, testing it on real data in a limited workflow, and measuring answer accuracy.
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Regular monitoring & re-testingChecking results against your business needs and adjusting the setup as your requirements change.
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Group Manager, Guesty
CIO at AB Stenströms Skjortfabrik
CEO at The African Touch
No. Adding a model is only one part of the setup. It still needs reliable data and a controlled way to work with your systems. AI Readiness covers those gaps, such as inconsistent data, system access, permissions, and controls around AI actions.
You need a business goal, not a finished use case. In the first steps, we help you narrow it to one to three specific use cases and the questions AI must answer.
We measure it against the use case you have chosen. For example, if AI needs to answer questions about sales, we check whether the relevant data is consistent and properly defined. We also check how AI will access that data and what it’s allowed to do with it.
No. You can use the AI platform or model you already have. We also work with different cloud and on-premises setups. The point is to make your existing systems usable for the AI work you have planned instead of forcing you to change your setup.
No. If you unify all your data in one place, it still does not fix conflicting formats or unclear definitions. One system might call something an “order” while another uses the same word differently. We sort out those differences and document what the relevant data means before AI uses it.