A large European media company set out to build a financial AI assistant for subscribers. The goal was simple: give readers fast answers on markets, stocks, and investment concepts.
They began with an autonomous agent that pulled market data, educational content, and news. What they needed next was clarity on how people would actually use it, plus confidence on accuracy and stability, and a plan to drive engagement.
Why they added analytics on day one
Before launching to real users, the team wanted full visibility into how the assistant would perform in the wild.
A demo of Nebuly showed clear, ready-to-use dashboards, options to keep data fully in their own infrastructure, and compliance standards fit for a media environment.
They decided to instrument analytics from the very start so they could build with real behavior in mind.
Building around real usage
With analytics instrumented from the start, the team could see exactly what early users were asking for. Patterns appeared fast.
- Specific stock information
- Current market movement updates
- Details on complex financial instruments
This view of real behavior let them focus their build on the features people actually used. Nebuly also gave them an early warning system for model failures, so issues could be spotted and fixed before they reached more users. The same data became the baseline for testing new ideas and tracking their impact during the beta.
Early results
The clearest technical win was response time. Average latency dropped from about 35 seconds to about 17 seconds. Analytics also worked as an early warning system for bugs and hallucinations.
Real usage data informed the roadmap. Planned work focused on stock specific search, guided education for novice investors, and decision support for experienced readers.
What this means for other teams
Instrument early. When analytics are in from day one, you see real jobs to be done, avoid features no one needs, and iterate faster while the product is still in beta. The team here built around actual behavior instead of assumptions, with clear signals for both experience and reliability.
Want to see these insights on your own copilot or assistant? Book a demo to see how Nebuly surfaces real usage patterns, adoption drivers, and drop off points from the first user session.
