A deep dive into a successful proof of concept for a context aware AI helper. I'll break down how a web scraper, a configuration engine, and an LLM came together to create a tool with huge potential for user support.
As engineers, we often get a front row seat to user frustrations. We see where they get stuck, where the documentation is lacking, and where a little bit of guidance could make a world of difference. I recently saw a clear opportunity to use AI to solve this problem by building an intelligent helper that knew where the user was and what they were trying to do.
I was so convinced of the potential that I built a full proof of concept (POC) to demonstrate its viability.
The Architecture: How It Worked
The idea was to provide real time, intelligent guidance that was specific to the user's exact context. The POC had three core stages:
Web Scraper for Context: The first piece was a scraper that could read the page's structure and content in real-time. This allowed it to identify the specific application, service, and even the section the user was currently interacting with.
Configuration Engine for Rules: The system would then use that context to load the specific rules and data relevant to that area. For example, in a FinTech application, it could load the unique configuration and guidelines for a specific lender's product that the user was viewing.
AI Integration (ETL for LLMs): Finally, all of this rich, contextual data would be "extracted, transformed, and loaded" into a prompt for a Large Language Model. The LLM's job wasn't to guess, but to use the precise information it was given to answer user questions and offer specific, accurate advice.
The Result: It Worked.
I built out a basic, working model for every stage of this process. Using anonymized but realistic data, I proved that the concept was not only viable but incredibly powerful. The potential was clear: this could be a single, intelligent support layer integrated across the dozens of services and lenders the company works with, dramatically reducing user friction.
Unfortunately, like many successful POCs in large organizations, the project was ultimately shelved due to shifting priorities. While it's disappointing that it hasn't moved forward, the experience of building it was a powerful lesson in the practical application of AI to solve real world business problems.