From information overload to clarity
Building an AI assistant to help professionals stay ahead (Part #1)
Hi, I’m Florian, Fractional CTO and builder of AI-powered tools designed to solve real, everyday problems.
Lately, I’ve been thinking about how much time we all spend just trying to stay informed. Reading articles. Following updates. Sifting through trends, laws, insights, research. It’s exhausting and for many professionals, it’s becoming unsustainable.
So I’ve started building a tool that I wish existed for myself and for everyone in the same boat.
The problem
Essential knowledge is buried in endless content streams.
Tabs open for “later” remain unread. Newsletters pile up. Algorithms bombard us with more than we asked for. The result? Too much time reading, not enough time applying.
The idea
I’m developing an AI assistant that will
- collect fresh content from multiple platforms every day;
- understand what each article or document actually says;
- match topics to your professional profile;
- rank relevance and create concise TL;DR summaries;
- deliver the most important updates straight to you, free of clutter.
Daily digest, weekly briefing, Monday-morning overview – whichever fits your rhythm, the goal is the same: stay ahead without feeling overwhelmed.
Who is it for?
The first implementation integrates with UPFLINX, where we support recruiters and HR teams. Yet the same technology extends far beyond hiring:
- Lawyers digesting legal changes
- Doctors and researchers scanning medical publications
- Executives needing fast industry briefings
- Academics surveying cross-disciplinary work
- Consultants monitoring environmental or policy shifts
Anywhere critical knowledge meets limited time, this assistant can help.
Why share the build?
As a Fractional CTO my mandate is to deliver tools that are smart and sustainable: low maintenance, high value, easy to slot into real workflows. I will document every step:
- concept and design decisions;
- architectural sketches and tech choices;
- obstacles and the fixes that worked;
- iterative improvements driven by feedback.
If you’re curious about applied AI or planning a similar product, you’ll be able to follow along and repurpose what I learn.
This article is just the beginning. In the coming weeks and months, I’ll be sharing more updates right here in the Behind the Build category – documenting the thinking, tools, problems, and insights that shape the development of this AI assistant. If you’re curious about how real-world AI tools are built step by step, or you’re working on something similar yourself, I invite you to check back regularly. Each post will offer something new – from technical deep dives to lessons learned and design decisions that make a difference.
Drop by from time to time and see how this tool evolves – or better yet, subscribe and follow along as the journey unfolds.