Beyond

Batch Processing
Model Evaluation
Company Explorer
Contradiction Events
dashboard
Signal dashboard
Company Universe
Proof of using Google AntiGravity

Inspiration

Large text data sources such as reports, disclosures, transcripts, and updates contain valuable signals. However, identifying these signals manually requires reading large volumes of text, which is time-consuming and inefficient. We were inspired by the challenge analysts face when trying to extract meaningful insights from massive text streams. Important information often exists, but finding it requires hours of manual effort. This motivated us to build Beyond, a system that automatically analyzes timestamped text feeds and extracts signals that help users prioritize what truly matters.

What it does

Beyond consumes a timestamped text feed and produces an actionable signal that helps users decide which documents deserve attention. The system processes incoming text using NLP techniques and generates a signal score that indicates the importance or risk level of each document. Instead of reading every document manually, users can rely on Beyond to highlight which items should be:

ignored
monitored
flagged for immediate attention

This helps reduce manual review time and enables faster, more informed decisions.

How we built it

We built Beyond as an end-to-end pipeline that turns raw text into a usable decision signal. First, we ingested timestamped text data from financial news and related text sources, along with stock price data used for validation. We cleaned, standardized, and merged the data so each document could be linked with its ticker, timestamp, and subsequent market movement. Next, we applied NLP techniques to extract meaningful features from the text. These included sentiment, domain-specific financial keywords, urgency indicators, named entities, and novelty scores based on how different a new document was from recent related documents. To capture semantic meaning and enable retrieval, we generated embeddings for each article and stored them in a vector database. This allowed us to identify similar past events, support novelty detection, and provide context-aware explanations. We then combined these text-derived features into a weighted signal score, which was mapped into an explicit decision rule such as NOISE, WATCH, ELEVATED, or CRITICAL. This made the system actionable rather than just descriptive. Finally, we validated the signal by comparing it with actual stock price movement after publication and exposed the entire workflow through a dashboard that showed incoming documents, signal levels, performance metrics, and AI-powered explanations.

Challenges we ran into

One of the biggest challenges was handling large volumes of text where the truly important signals were buried inside routine or repetitive language. Not every article with strong wording led to meaningful impact, so separating signal from noise was difficult. Another challenge was making the output genuinely actionable. It was not enough to score text; we needed a clear and defensible decision rule that translated the score into something a user could act on. We also had to ensure that the system respected timestamp ordering. Since this is a feed-based problem, we had to avoid using future information when generating current signals. Finally, validating usefulness was challenging because text analysis alone is not enough. We had to connect our signals to measurable outcomes and compare them against simpler baselines to show that the system added real value.

Accomplishments that we're proud of

We are proud that we built a complete pipeline from ingestion to decision-making rather than just an isolated model. We successfully transformed raw timestamped text into a structured signal that can help users prioritize what to review first. That means Beyond does not just analyze text, it supports decisions. We are also proud that we combined multiple components such as NLP, vector search, signal scoring, evaluation, and dashboarding into a single working system. Another accomplishment was making the project understandable to both technical and non-technical users. The final output is simple to interpret while still being grounded in a real backend pipeline.

What we learned

This project taught us that extracting value from text is not just about understanding language, but about connecting language to action. We learned that large text streams contain useful signals, but manual review does not scale. Even simple automation can save significant time when it helps users focus on the most important items first. We also learned that a strong system needs more than good NLP. It needs a clear decision rule, time-aware processing, and a way to prove that the output is useful. Most importantly, we learned that explainability matters. Users trust signals more when they can understand why something was flagged and how it compares to similar past events.

What's next for Beyond

The next step for Beyond is to make the system more robust, more real-time, and more adaptive. We want to improve the signal engine with stronger embedding-based and finance-specific models, explore signal decay over time, and better calibrate confidence scores so the alerts become more reliable. We also want to expand Beyond into a live monitoring system that continuously tracks incoming text streams and updates signals in real time. In the longer term, we see Beyond evolving into an intelligence layer for large text feeds, helping users not only detect important signals faster, but also understand why they matter and what action should come next.