When we first started building Ask Linc, we did what most teams building AI products do: we picked a single model and built everything around it.
That worked… for a while.
But as Ask Linc grew, we realized something important:
No single model is best at everything.
Different AI models have different strengths — reasoning, long-context analysis, structured data interpretation, cost efficiency, and response reliability. Because Ask Linc analyzes complex financial data and answers nuanced questions about your finances, those differences actually matter a lot.
So we made a deliberate change to our architecture.
Instead of relying on one model, Ask Linc now routes questions to the models best suited to answer them.
And two models consistently performed the best for our use cases: Claude and Gemini.
Here’s why.
Most financial questions aren’t simple lookups. They’re analytical.
Examples we see every day:
“Are we still on track to retire?”
“How much house can we afford?”
“What withdrawal rate is safe given my portfolio?”
“How would tariffs or inflation impact my finances?”
These questions require the model to:
interpret structured financial data
combine it with macroeconomic context
run multi-step reasoning
explain the result clearly
In our testing, Claude consistently produced the most reliable reasoning chains for these kinds of questions, especially when combining multiple data sources.
That makes it particularly strong for Ask Linc’s core use case: thinking through financial decisions.
A typical Ask Linc query might include:
dozens of financial accounts
multiple investment portfolios
historical balances
user preferences and goals
macroeconomic data
market summaries
That’s a lot of context.
Claude and Gemini both handle very large context windows well, which allows Ask Linc to feed the model the full financial picture instead of forcing us to aggressively summarize or truncate your data.
The result is more accurate answers.
Financial data is highly structured:
transactions
account balances
asset allocations
cash flow
investment holdings
Gemini performed particularly well in tests where the model needed to:
interpret structured tables
compare values across accounts
summarize trends
identify anomalies
For questions that require fast analysis across structured financial data, Gemini has been a strong performer.
One thing we care deeply about is consistency.
Financial analysis tools can’t produce wildly different answers depending on the model’s mood that day.
Claude and Gemini both showed:
lower hallucination rates
more stable outputs
better adherence to instructions
This matters when users are asking high-stakes questions about money.
Instead of forcing one model to do everything, Ask Linc now uses model routing.
That means the system evaluates the question and sends it to the model best suited for the job.
For example:
deep reasoning → Claude
structured data analysis → Gemini
lightweight tasks → smaller models
This approach gives users:
better answers
faster responses
lower infrastructure cost
It also future-proofs the platform. As new models improve, we can integrate them without rebuilding the entire system.
Switching models wasn’t about chasing hype.
It was about improving the quality of answers Ask Linc provides.
Our goal isn’t just to show you dashboards or charts.
It’s to help you answer questions like:
Are we actually on track financially?
What happens if markets drop 30%?
Can we afford this house without jeopardizing retirement?
Those questions require real analysis — not just surface-level responses.
Using the best models available helps us deliver that.
Most users will never notice the change directly.
But under the hood, Ask Linc is now:
smarter about how questions are answered
better at analyzing complex financial situations
more adaptable as AI models evolve
And that’s the direction we believe AI products should move:
Not one model doing everything, but the right model for the job.
If you want to see how it works, try asking Ask Linc something real:
“Are we still on track to retire?”
“Can we afford a $1.2M house?”
“What happens if markets drop 20% next year?”
Those are the kinds of questions this architecture was built for.
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