An AI assistant can help you design a table or explore a business idea. Getting it to work with information arriving every day requires another set of decisions: where the data lives, how it gets there, who can access it and what happens when something fails.
Start with one useful task. Preparing business data for AI does not mean migrating every system or training your own model. It means making the information needed for that task available, current and understandable, with appropriate access.
1. Define the work before choosing infrastructure
Write down the question or action the application must support. “Help our team review incoming deals” is more useful than “put our data into AI.” Identify who will use the result, how they will check it and which source is authoritative when records disagree.
Check whether a product you already use has a suitable integration. If it does, configuration and testing may be enough. Custom infrastructure becomes useful when the existing options leave a specific gap.
2. Make a small data-readiness map
For each source, record the following. The examples are illustrative, not a description of a client's private system.
| Question | Example decision to record |
|---|---|
| Where does it come from? | A provider sends a JSON file; the provider's record ID identifies each item. |
| What does each field mean? | Amounts have an explicit currency; dates distinguish the event time from the import time. |
| How current must it be? | The application shows the latest successful import time and flags stale data. |
| Who can use it? | Named roles can retrieve approved fields; changes require separate permission. |
| What happens to a bad record? | It goes to a review queue with a reason, without silently disappearing. |
| Who owns recovery? | An assigned operator receives a failure alert and follows a documented recovery procedure. |
3. Make repeated imports predictable
Separate reliable data movement from model judgment. When an input already has a defined structure, an import can validate and map fields using ordinary code. An AI model does not need to decide whether the same provider record should be inserted twice.
Agree what should happen when the provider resends a file, changes a field, omits a value or stops delivering data. Keep a record of accepted and rejected items so you can reconcile an import with its source. Preserve source context where it is needed to investigate an answer later.
For 101, a real-estate investment firm, we evaluated a plan its founder had developed with Claude, retained the useful parts and revised the parts that did not fit. We then implemented the ingestion pipeline and database for incoming deal information, creating a foundation agents could work from. It demonstrates a specific data-foundation implementation; it is not a claim about automated investment decisions or measured financial returns. See the implementation and its scope.
4. Choose how an agent will access the information
Structured records may suit defined database queries or an existing API. Documents may need a retrieval approach that preserves context and source references. A vector database is an option for some retrieval needs, not a requirement for every AI project.
An existing connector, an API, a command-line interface or an MCP integration may expose the required operations. Choose around the application and the controls available. Start with the minimum access the task needs, set sensible query limits and test unauthorized requests. Review and approval should be part of the design where an action changes consequential business records.
5. Test the foundation as well as the answer
- Completeness: Can you account for the records delivered, accepted and rejected?
- Repeatability: Does resending the same input produce the agreed result rather than unwanted duplicates?
- Freshness: Can a user tell when the underlying information was updated?
- Access: Are requests outside the user's permission refused?
- Recovery: Can the responsible person restore or replay the data flow under the agreed recovery procedure?
- Usefulness: Does the application answer representative business questions correctly enough for its intended role?
A successful database connection alone does not establish any of these outcomes. Set acceptance criteria before the build and retain the test results for handover.
Scope a first step you can operate
Choose one source, one business task and one accountable owner. Include hosting, permissions, scheduled jobs, monitoring and support in the scope—not just the initial import. Agree ownership of accounts and custom deliverables, alongside any provider dependencies.
If this is the gap between your AI idea and daily use, explore our AI data integration and infrastructure services. Bring a high-level description of the source and task; we can agree how to handle samples after the initial conversation.
