Ask Your Data a Question: How AI Filters Make Everyone a Power User

You know the information is in there somewhere. You just need to find it.

Maybe it's all the orders placed last month that haven't shipped yet. Or the clients who haven't been contacted in ninety days. Or the invoices over a certain value that are coming due next week. The data exists in your system — the question is how quickly you can extract it.

For too long, the answer has depended on technical skill. Finding specific records has meant understanding filter interfaces, knowing which operators to use, and constructing logical conditions correctly. It's created a two-tier system: power users who can get answers instantly, and everyone else who needs to ask someone for help.

Kinabase's AI Filters change that equation entirely.

The Traditional Way

Think about how filters typically work in business software. You open a filter panel, select a field from a dropdown, choose a condition (equals, contains, greater than, less than), and enter a value. For simple queries, that's manageable. For anything more complex, it gets challenging quickly.

Want to find records where status is "pending" AND the value is over £5,000 AND the due date is within the next week? That's three conditions that need combining correctly. Want to exclude certain categories? Add more conditions. Want to see records that match one set of criteria OR another? Now you're dealing with nested logic.

Most people don't build filters like this because it's tedious and error-prone. Instead, they scroll through records manually, export everything to Excel and filter there, or ask a technically-minded colleague for help. None of these are ideal. They waste time, interrupt others, and often don't even produce the right answer.

Just Describe What You Want

AI Filters take a radically different approach. Instead of constructing filters through menus and dropdowns, you simply describe what you're looking for in plain English.

"Show me overdue invoices"

"Clients we haven't contacted in the last 90 days"

"Orders over £1,000 that are still pending"

"Projects due next week that aren't marked complete"

Type your description, and Kinabase translates it into the appropriate filter conditions. The results appear instantly, and you can see exactly what filter was applied. If the AI interpreted your request slightly differently than you intended, you can refine your description or adjust the generated filter directly.

This isn't a separate "AI mode" buried in menus. It's integrated into the standard filter interface. Where you'd normally start building conditions, you can instead type a description of what you want. The system understands which approach you're taking and responds accordingly.

Why This Matters for Business Teams

The implications for how teams interact with their data are significant.

Non-technical staff become self-sufficient. The administrator who needs to check on late deliveries doesn't need to interrupt the operations manager. The sales rep looking for promising leads doesn't need to request a custom report. Anyone who can describe what they're looking for can find it themselves.

Decision-making becomes faster. When questions arise in meetings ("How many complaints have we had about that supplier this quarter?"), someone can pull the answer in seconds rather than promising to follow up later. The pace of discussion isn't limited by data accessibility.

Ad-hoc analysis becomes practical. Not every question justifies creating a saved view or a formal report. Sometimes you just want to quickly check something and move on. AI Filters make that kind of exploratory questioning viable where before it might not have been worth the effort.

Training burden decreases. New team members can start finding the data they need from day one, rather than spending weeks learning how the filter system works. The interface meets them where they are rather than demanding they learn a new syntax.

Real Examples in Practice

Let's walk through how this works in various contexts:

In finance, you might type "invoices due in the next 7 days with value over £10,000" to identify significant payments coming up. Or "unpaid invoices from Q3 2025" to check on aged debt. The finance team can answer their own questions without building complex filter trees.

In operations, try "orders placed last week that haven't been dispatched" to identify bottlenecks. Or "suppliers with more than 3 returns this month" to spot quality issues. Getting these answers used to require either technical skill or patience.

In sales, "leads assigned to me that I haven't contacted in 30 days" helps manage pipeline discipline. "Opportunities over £50,000 closing this quarter" focuses attention on the deals that matter most.

In HR, "employees whose certifications expire before April" flags training needs. "Absences in the engineering team last month" provides patterns for resource planning.

In each case, the query is expressed naturally, and the system handles the translation into precise filter logic.

Understanding the Results

Transparency is important. When AI Filters generate a filter from your description, you can see exactly what conditions were applied. This serves several purposes:

Verification: You can confirm the filter matches your intent. If you asked for "high-value opportunities" and the filter shows value > £5,000, but you actually meant > £50,000, you can adjust accordingly.

Learning: Seeing the generated filters helps you understand how your data is structured. Over time, users often pick up filter-building skills organically.

Editing: Sometimes the AI gets you 90% of the way there. You can fine-tune the generated conditions rather than starting from scratch or rephrasing your request.

Getting Started

AI Filters are available on Kinabase Pro plans. You'll find the capability integrated into the filter interface on any collection — look for the text field that invites you to "Describe a filter."

To get good results:

Be specific about what you want to see. "Recent orders" is vague; "orders from the last 14 days" is precise. The more specific your description, the more accurate the filter.

Reference your actual field names when you can. If you know your collection has a "Status" field, mentioning status in your query helps. But don't worry if you don't remember — the system is fairly good at figuring out what you mean.

Think in terms of conditions. You're essentially describing filter criteria, so framing your query as "records where X is true" tends to work well.

Iterate if needed. If the first result isn't quite right, refine your description or edit the generated filter. The system isn't perfect, but it's usually close enough to be useful.

Democratising Data Access

There's a broader principle at play here. When data access depends on technical skill, organisations develop information bottlenecks. The people who know how to query the system become points of congestion. Others either wait for help or work with incomplete information.

AI Filters don't just save time for individual queries. They fundamentally change who can get answers and how quickly. The sales manager can verify their own pipeline numbers before the meeting. The customer service lead can pull their own stats for the weekly review. The director can explore the data themselves rather than waiting for a report.

This democratisation has cultural effects too. When everyone can interrogate the data, conversations become more data-informed. Claims can be verified. Assumptions can be tested. The collective intelligence of the organisation increases.

The Practical Promise of AI

This is what practical AI looks like. Not science fiction, not magic, just thoughtful technology that removes friction from everyday work. The underlying capability — translating natural language into structured queries — is sophisticated. But the experience for users is simple: describe what you want, get results.

That simplicity is deliberate. We believe AI should make powerful capabilities accessible to everyone, not add another layer of complexity that only experts can navigate. AI Filters are useful because they disappear into the workflow. You don't think about the technology; you just find what you're looking for and move on.

For operations managers and business owners, that's exactly the point. Technology that helps you work, not technology that becomes work in itself.

Want to see AI Filters transform how your team works with data? Book a demo and we'll show you what's possible.