AEGIS X AI Investigation Agent

Inspiration

We built AEGIS X 3.0 because working with data is not just about finding numbers — it is about understanding what those numbers mean and whether we can trust them.

When datasets contain missing values, duplicate records, or unusual patterns, finding these issues manually can take a lot of time. AI can help explain data, but its answers are not always reliable unless they are supported by actual evidence.

This inspired us to create AEGIS X, a platform that combines data analysis with AI to make investigations faster, clearer, and more transparent.

Our core idea is simple: Calculate with Code. Explain with AI. Verify with Evidence.

What it does

AEGIS X 3.0 is an AI-assisted data investigation platform that helps users explore datasets, identify potential problems, and understand findings without needing to perform every check manually.

Users can upload CSV files or import data from supported public URLs. The platform analyzes the data to identify missing values, duplicate records, statistical outliers, and inconsistencies in business rules.

Its AI Investigation Copilot helps users ask questions about their data in natural language and understand the results. Finding IDs and row references help connect explanations to the underlying evidence.

The platform also supports data quality scoring, real-time response streaming, investigation summaries, and exportable reports. When AI credentials are unavailable, a deterministic rule-based engine can provide supported analysis without requiring an AI API key.

Our goal is to make data investigation easier while keeping the results understandable and verifiable.

How we built it

We built AEGIS X using Node.js and Express.js for the backend, with HTML, CSS, and JavaScript powering the frontend dashboard.

We designed the application around three main components:

Data analysis: Performs statistical calculations, data quality checks, duplicate detection, and validation rules.
AI Copilot: Uses configured AI providers, including Google Gemini, to explain findings and answer questions using available investigation context.
Evidence system: Connects findings to relevant records, rule thresholds, and verification details.

We also incorporated Server-Sent Events (SSE) for streaming responses and designed support for Firebase Authentication and Google Cloud Firestore for account management and investigation persistence.

A major design decision was to keep calculations separate from AI-generated explanations. This allows the platform to calculate results using code and then use AI to make those results easier to understand.

Challenges we ran into

One of our biggest challenges was figuring out how to make AI-assisted analysis more trustworthy. A convincing explanation is not necessarily a correct one, so we designed the workflow around calculated results and traceable evidence.

We also had to consider how to analyze different datasets, since column names, formats, and business rules can vary significantly.

Other challenges included integrating multiple AI providers, managing streaming responses, handling invalid data, and planning secure storage for uploaded datasets and investigation results.

Balancing advanced functionality with a simple user experience was another important consideration. We wanted the platform to be useful for both technical users and people who are less familiar with data analysis.

These challenges helped shape our focus on modular development, transparent calculations, and evidence-based explanations.

Accomplishments that we're proud of

We are proud of the idea behind AEGIS X and the way it brings data investigation and AI-assisted explanations together in one platform.

Some of the key aspects we are working toward include automated data profiling, statistical anomaly detection, evidence-linked findings, natural-language interaction, and flexible AI integration.

We are especially proud of our guiding principle: AI should help users understand the evidence rather than replace it.

AEGIS X represents our effort to make data analysis more accessible while encouraging users to question results, investigate unusual patterns, and verify conclusions before taking action.

What we learned

This project taught us that building an AI application involves much more than connecting an API to a chatbot.

We learned about the importance of statistical analysis, backend architecture, API integration, real-time communication, data validation, and secure handling of credentials.

We also gained a better understanding of why AI-generated answers need reliable context and why investigation results should be traceable to their original data.

One of our biggest takeaways is that trustworthy AI depends not only on the model but also on the quality of the data, the calculations behind the results, and the evidence available to support an explanation.

What's next for AEGIS X — Open AI Investigation Agent

We want to continue developing AEGIS X into a more complete and reliable data investigation platform.

Our next steps include improving anomaly detection, adding dataset version comparisons, expanding investigation history, and creating detailed PDF evidence reports.

We also plan to strengthen authentication and access controls, improve AI evidence validation, introduce safe SQL-based investigation, and expand support for additional data sources.

Over time, we hope to make AEGIS X useful for data analysts, developers, researchers, and organizations that need a clearer way to investigate data quality issues and understand unusual patterns.

AEGIS X 3.0 — From Raw Data to Trusted Insights.