How do I make years of old company data searchable with AI?
Short answer: Start with the questions your staff keep asking, not with connecting every system. Gather data from the platforms and servers that answer those questions, label it consistently, then add an AI search layer that answers with source references. Expect cleanup of messy data to be most of the work.
The usual starting point
Years of records sit across different platforms, spreadsheets, chat apps and a local server. Everyone knows the information exists. Nobody can find it quickly, so people ask the longest serving colleague instead.
A practical sequence
1. List the 20 questions people ask most. "What did we quote this client last time?" "How did we fix this fault?" "Which policy documents does this customer have?"
2. Map where each answer lives. Platform, folder, server, or someone's phone.
3. Decide what is in scope. Start with one or two sources, not all of them.
4. Clean and label. Consistent names, dates, client or job IDs. This step decides whether search works.
5. Add the AI layer. It should answer from your documents and show which document it used, so staff can verify.
6. Test with real questions from real staff before rolling out.
7. Set who can see what.
Risks to plan for
- Privacy and permissions: client and policy data are sensitive. Check your obligations under Malaysia's PDPA and your industry's rules before putting data into any AI tool.
- Wrong answers: AI can answer confidently from incomplete data. Require source references and spot check.
- Data quality: if records are inconsistent, search will be inconsistent.
- Local servers: decide whether data stays on premise or is copied to a cloud service, and who approves that.
Where this connects to workflow
Search is the first layer. The second layer is workflow: when something happens (a new claim, a repeat fault, a renewal due), the system retrieves the right history and prompts the right action. Lobang Lab is currently building this combination of workflow and an AI layer for an insurance agency and a machinery manufacturer.
Related: How do I stop losing company knowledge when staff leave? | What is a trigger library?
Lobang Lab is a community founded by Chris Phang that helps experienced business owners turn what their people know into searchable, transferable systems with AI, so the business keeps its knowledge when staff leave or the founder steps back.