Ask a fintech analyst whether they need help writing SQL and you'll usually get a smile. That's not where the real time goes. The hard part is deciding what the query should represent in the first place: what definition to use, which segments matter, what changed, and why it changed. That's the gap between "SQL chat" and "reasoning."
SQL chat is great at translating a question into a query and returning a table. Reasoning is about turning a question into an explanation you’d be comfortable making a decision on.
Same question, two very different outcomes
Imagine approval rate drops. You ask what happened.
A SQL‑chat tool will translate your prompt to a query, execute it, and return a number. If you push further, you’ll get more queries and more numbers. Useful, but you’re still doing the stitching: which denominator is correct for this context, which segments moved, what policies changed mid‑month, and whether this is seasonality or signal.
A reasoning engine takes a different path. It starts with how your team actually defines the metric. It brings in relevant context, like policy changes, applicant mix, and historical patterns, without you having to enumerate them one by one. And then it gives you a narrative: what moved, why it moved, and what's worth checking next. If you correct it, it learns.
Why context, explanation, and learning matter
Decisions don't live in a vacuum. Risk needs to understand causality. Product needs to know whether to ship the change or roll it back. Finance wants the revenue impact. A table alone doesn't do that. A good explanation does. It shows the chain from definition to data to justification.
Reasoning systems earn their keep by remembering the definitions you use, exposing their logic as they go, and adapting to your feedback so the next pass starts closer to your standard. Over time, that learning compounds. You spend fewer cycles on “what does this mean?” and more on “what do we do about it?”
What this looks like in practice
When we built InsightAssist, we didn’t try to replace SQL or dashboards. We focused on the step that usually happens off to the side: the analyst’s reasoning loop. That means:
- using the definitions that matter to you, not generic guesses;
- bringing the relevant context forward instead of making you chase it down;
- explaining the why as clearly as the what; and
- learning from your corrections so the second answer is better than the first.
It’s a small shift with an outsized effect. You still get numbers. But you also get a story you can stand behind.
If that’s the kind of help you want in your workflow, we’d love to show you how it feels with your data.