DEV Community: Doru Prodan

Building an AI Research Desk: Multi-Agent Systems in Fintech

Doru Prodan — Fri, 03 Jul 2026 18:51:05 +0000

Beyond Spreadsheets: The Rise of the AI-Powered Research Desk

For decades, financial analysis was the domain of Excel wizards and Bloomberg Terminal power users. But for developers and data engineers, the manual labor of sifting through 10-Ks, parsing news sentiment, and tracking insider trading via SEC Form 4s feels like a problem begging for automation.

We are currently witnessing a shift from manual research to agentic AI workflows. Platforms like StockAdvisor360 are leading this charge, moving beyond simple chatbots to "multi-agent" systems that debate, analyze, and synthesize complex financial data.

In this article, we’ll dive into the technical architecture of an AI research desk, explore how to handle unstructured financial data, and look at real-world case studies—like the recent shifts in Amphenol (APH) and Intel (INTC)—through the lens of automated analysis.

The Architecture of a Multi-Agent Stock Analyst

A single LLM prompt is rarely enough for high-stakes financial decisions. Hallucinations are a risk, and context windows can be overwhelmed by thousands of pages of SEC filings. The solution is a Multi-Agent System (MAS).

1. The Technical Agent

This agent focuses on quantitative data. It pulls from APIs (like Alpha Vantage or Polygon.io) to analyze RSI, moving averages, and volume trends. It doesn't care about "vibes"; it cares about price action.

2. The Fundamental Agent

This agent is responsible for parsing the balance sheet. Its job is to ingest SEC filings. In a developer's workflow, this involves using RAG (Retrieval-Augmented Generation) to query a vector database of indexed 10-K and 10-Q filings. For example, when Amphenol (APH) acquired CommScope CCS, a fundamental agent would immediately flag the shift in capital expenditure toward AI data center infrastructure.

3. The Sentiment/News Agent

Using NLP, this agent monitors the "Stock Pulse." It processes thousands of headlines to determine if a stock's movement is driven by macro trends (like the "chip wreck" affecting Intel) or company-specific catalysts.

4. The Moderator Agent

This is where the magic happens. The Moderator agent takes the conflicting reports from the Technical, Fundamental, and Sentiment agents and forces a "debate." If the Technical agent sees a "Buy" but the Sentiment agent sees a "Chip Wreck" (as seen with INTC recently), the Moderator synthesizes these into a balanced verdict.

Solving the SEC Filing Problem

One of the biggest hurdles for developers in the fintech space is the SEC’s EDGAR system. While the data is public, it is notoriously messy.

To build an effective SEC filing summary tool, you need a pipeline that looks something like this:

Ingestion: Scrape or stream filings via the SEC's RSS feeds.
Preprocessing: Convert HTML/XBRL into clean Markdown or text.
Chunking: Break down a 200-page document into semantically meaningful chunks (e.g., "Risk Factors," "Management Discussion").
Vectorization: Store these in a database like Pinecone or Weaviate.
Querying: Use an LLM to summarize specific sections, such as identifying routine administrative filings vs. major strategic shifts.

For instance, in the case of NVIDIA (NVDA), an AI agent can quickly distinguish between a standard Form 4 (insider withholding shares for tax purposes) and a more significant Form 144 (intent to sell). To a human, these look like a wall of legalese; to a regex-powered AI parser, they are structured data points.

Case Study: Analyzing Market Volatility with AI

Let’s look at how an AI research desk handles real-world scenarios based on recent market data:

Amphenol (APH): The Strategic Pivot

While many investors were focused on general tech, AI agents identified APH’s strategic positioning in the AI data center market. By analyzing the CommScope CCS acquisition, the AI could correlate this move with the broader AI-led growth trend, leading to increased analyst confidence and raised price targets from firms like Barclays.

Intel (INTC) vs. The "Chip Wreck"

When Intel's stock began to sink alongside competitors, a sentiment agent would have flagged the "chip wreck" narrative. By connecting Meta's aggressive AI Cloud push to the competitive pressure on Intel's margins, the AI provides a macro-context that a simple price-tracking bot would miss.

Verizon (VZ): Symbolic vs. Fundamental Changes

On June 29, Verizon was removed from the Dow Jones Industrial Average. A developer-centric analysis tool would track the resulting "selling pressure" from index-tracking funds. However, the AI also flags the competitive threat from SpaceX’s Starlink. This dual-layered analysis—technical (index removal) vs. fundamental (disruptive tech)—is exactly what a multi-agent system excels at.

Why Developers Should Care

Building these systems from scratch is expensive. Between OpenAI API costs, vector database hosting, and financial data subscription fees (which can run into the thousands), the "build vs. buy" debate is heavily tilted toward specialized platforms.

Platforms like StockAdvisor360 offer a "Research-as-a-Service" model. For a developer, this is essentially an API for high-level financial intelligence. Instead of building the scraper, the parser, and the multi-agent logic, you can access the output of these agents for a fraction of the cost ($1.99 per report).

Key Takeaways for Technical Investors

Agentic Workflows > Single Prompts: When analyzing stocks, use a system that incorporates multiple perspectives (Technical, Fundamental, Sentiment).
RAG is Essential: Don't let an LLM guess about a company's debt; make it retrieve the data from the latest SEC filing.
Watch the Metadata: Insider trading (Form 4) and index changes (like VZ's removal from the Dow) are leading indicators that sentiment analysis often misses.
Automation is the Moat: In a market where "chip wrecks" and "AI bubbles" can shift sentiment in hours, having an automated research desk allows for faster, data-driven pivots.

Conclusion

The intersection of LLMs and financial data is one of the most exciting frontiers for developers. Whether you are building your own pipeline using LangChain and Python or leveraging an established AI research desk, the goal remains the same: reducing the noise and finding the signal in a sea of unstructured data.

Ready to see a multi-agent system in action? You can run your first AI-powered stock analysis for free and see how a team of AI agents debates your favorite ticker.

Check out StockAdvisor360 to start your research.

Building an AI-Powered Bill Splitter: OCR, LLMs, and Real-time State

Doru Prodan — Sun, 18 Jan 2026 11:35:59 +0000

The Developer’s Dilemma: The Post-Lunch Math Problem

We’ve all been there. A team lunch ends, a long receipt arrives, and suddenly everyone is squinting at tiny font sizes, trying to calculate tax and tip for their specific order of truffle fries and a club sandwich. For developers, this isn't just a social annoyance; it’s a logic problem begging for an automated solution.

This is the problem space that Hackbill occupies. By leveraging AI-powered scanning and real-time synchronization, it eliminates the manual overhead of debt collection among friends. In this article, we’ll dive into the technical architecture required to build a high-performance bill-splitting engine, focusing on OCR pipelines, LLM-based data structuring, and real-time state management.

1. The OCR Pipeline: From Pixels to Raw Text

The first challenge in building a tool like Hackbill is converting a potentially blurry, low-light smartphone photo into machine-readable text. Traditional Optical Character Recognition (OCR) has come a long way, but receipts present unique challenges: variable fonts, crumpled paper, and complex layouts (columns for quantity, name, and price).

The Pre-processing Step

Before hitting an OCR engine, the image usually needs a pipeline to improve accuracy:

Grayscale Conversion: Removing color noise.
Perspective Correction: Using edge detection (like Canny) to find the receipt corners and perform a 4-point perspective transform.
Adaptive Thresholding: Handling uneven lighting across the paper.

Choosing an Engine

While Tesseract is the open-source standard, modern cloud-based solutions like AWS Textract or Google Cloud Vision provide better results for multi-column layouts because they return "blocks" and "forms" rather than just raw strings.

2. Using LLMs for Structured Data Extraction

Raw OCR output is often a mess of unstructured strings. For example, a line might read 1 BU RGER $15 .00. Traditional regex-based parsing is incredibly brittle here because every POS system formats receipts differently.

This is where Large Language Models (LLMs) like GPT-4o or Claude 3.5 Sonnet shine. Instead of writing 500 lines of regex, you can pass the raw OCR text to an LLM with a system prompt designed to return a JSON schema.

Example Implementation (Node.js)

const extractReceiptItems = async (rawText) => {
  const prompt = `
    Extract the items, quantities, and prices from this receipt text.
    Return ONLY a JSON array of objects with keys: name, quantity, price.
    Text: "${rawText}"
  `;

  const response = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: prompt }],
    response_format: { type: "json_object" },
  });

  return JSON.parse(response.choices[0].message.content);
};

By using AI-powered scanning, Hackbill can intelligently identify which lines are items and which are metadata (like the date or the server's name), significantly reducing the "Review" phase for the user.

3. Real-time Collaboration and State Management

Once the receipt is scanned and items are extracted, the next technical hurdle is the "Share and Claim" phase. In a developer-centric view, this is a distributed state problem. If three people are looking at the same bill, we need to ensure that two people don't claim the same beer at the same time.

The Tech Stack for Live Syncing

To achieve the "see who's claiming what live" feature mentioned in the Hackbill workflow, you generally have three options:

WebSockets (Socket.io): Best for low-latency, bidirectional communication.
Server-Sent Events (SSE): Good for one-way updates (server to client).
Real-time Databases (Supabase/Firebase): The most efficient for rapid development, as they handle the pub/sub logic out of the box.

Handling Conflicts

When a user clicks "Claim," the client should send an optimistic update to the UI while the backend validates the request. If the item is already claimed by another user_id in the database, the backend rejects the transaction, and the UI rolls back.

4. The Math of Fair Tip Distribution

One of the most innovative features of the Hackbill philosophy is Fair Tip Distribution. Most people simply split the tip evenly, but that’s technically unfair. If I ordered a $5 salad and you ordered a $50 steak, an even split of a $10 tip means I’m overpaying significantly relative to my consumption.

The Algorithm

Hackbill ensures only people who claimed items pay their share of the tip. Mathematically, this is calculated as a weighted percentage:

Calculate Subtotal: Sum of all claimed items.
Calculate User Subtotal: Sum of items claimed by User A.
Calculate Proportion: User A Proportion = User A Subtotal / Total Subtotal.
Apply Tip/Tax: User A Total = User A Subtotal + (Total Tip * User A Proportion) + (Total Tax * User A Proportion).

Implementing this logic in your backend ensures that the final "Claim" amount is mathematically sound and socially frictionless.

5. Security and Privacy Considerations

As developers, we must consider the sensitivity of receipt data. Receipts often contain the last four digits of a credit card, the merchant's address, and dining habits.

Data Minimization: Only store the item names and prices. Discard the raw image after successful parsing.
Short-lived Sessions: Use unique, UUID-based URLs for sharing bills that expire after a set period (e.g., 24 hours).
Encryption: Ensure all data in transit is handled via HTTPS and PII (Personally Identifiable Information) is encrypted at rest.

Conclusion: Solving Social Friction with Code

Building a tool like Hackbill is a masterclass in combining various modern technologies—Computer Vision, Natural Language Processing, and Real-time Web Systems—to solve a mundane human problem.

For developers looking to build similar applications, the takeaway is clear: don't fight the unstructured nature of the real world with rigid regex. Embrace LLMs for data extraction, use real-time sync for collaboration, and always ensure your math handles edge cases like weighted tip distribution.

Ready to stop doing manual math? Check out Hackbill to see these technical principles in action and streamline your next group dinner.

Key Takeaways for Devs:

OCR is the start, not the end: Use LLMs to structure the messy data OCR produces.
Real-time is non-negotiable: Use WebSockets or Supabase for a seamless "claiming" experience.
Weighted Math > Simple Math: Always calculate shares based on proportional subtotal to ensure fairness.