A real-time platform that pulls financial news, market data, and analytics from many sources into one personalized feed, so users stop manually cross-referencing multiple sites for the information that matters to them.
Financial professionals and active investors track dozens of sources — news outlets, company filings, market data feeds, and analyst commentary — to stay current on the assets and sectors they follow. Doing that manually doesn't scale: relevant information is scattered, time-sensitive, and easy to miss.
FinanceAI is a content aggregation and personalization platform that pulls financial content from multiple sources and applies machine learning to rank and surface what's relevant to each user, in real time.
The client needed one personalized view of financial news and analytics instead of a generic feed everyone sees the same way. That meant solving two problems at once: continuously collecting content from a wide range of external sources, and then determining — per user, per session — which of that content actually matters.
A generic "latest news" feed wasn't the goal. The system had to connect content to the specific companies, sectors, and instruments each user follows, and do it fast enough to stay useful in a market that moves throughout the trading day.
We built an aggregation layer that ingests financial content from multiple external sources and a machine learning layer on top of it that scores and ranks that content for relevance to each user's tracked companies, sectors, and interests — surfacing a personalized, real-time feed rather than one generic stream for everyone.
At a high level, the system has three layers: an ingestion layer that continuously pulls content from external financial data and news sources, a scoring/ranking layer that applies machine learning to match content to individual user interests, and a delivery layer that serves the personalized feed to the product's frontend in real time.
Deeper technical specifics (exact data sources, model type, and whether any generative/LLM component or RAG is involved) aren't confirmed in the material we had on hand for this page. Rather than guess, we've left this section at the level we can verify — see the TODO in the page source for what's needed to expand it accurately.
Financial content sources vary in format, update frequency, and reliability. The ingestion layer had to normalize content from multiple sources into a consistent structure the ranking layer could work with, without losing time-sensitive information to processing delays.
Scoring content against each user's specific interests — rather than showing the same feed to everyone — had to happen continuously and quickly enough to stay useful as markets move during the trading day, not as a periodic batch job.
FinanceAI is a live, production platform delivering real-time, personalized financial insights to its users today.
We don't have client-approved usage or performance metrics on file for this project. If you can share verified numbers (users served, content volume processed, latency, etc.), we'll add them here — see the TODO in this section for what would be useful.
Tell us what you're working on and we'll discuss the technical approach, scope, and next steps.
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