Building briefstock.ai: Real-Time Data at Indie-Dev Scale

Analyzing public equities is a time-consuming process. Retail investors often spend hours reading financial statements, digging through balance sheets, and running DCF models to determine if a stock is fairly valued. We built briefstock.ai to automate this research, packaging it into a professional stock report generated in seconds.

The challenge wasn't just generating text; it was calculating real financial metrics at scale without a enterprise budget. Here is how we built it.

The Brief

We wanted briefstock.ai to serve as a fast, high-quality replacement for the manual stock research process. The app needed to generate comprehensive fundamental analysis reports on demand. The core product requirements included:

• Dynamic coverage of thousands of equities.
• Report generation in under a minute to preserve the user experience.
• 8 detailed report sections covering valuation, risks, and technicals.
• Real math-based calculations like Discounted Cash Flow (DCF) and Free Cash Flow (FCF) analysis.
• An AI-powered sentiment and narrative synthesis that reads like a human analyst.

The Scoping Phase: Narrowing the Output

When we first scoped briefstock.ai, we imagined an interactive chat interface where users could ask open-ended questions about stocks.

We quickly realized that open-ended chat is a product risk. It is slow, difficult to format, and prone to LLM hallucinations. If a user asks "what was Apple's Q3 revenue?" and the LLM guesses the number, the credibility of the entire platform is ruined.

We refocused the product on a fixed, structured report. By replacing the chat window with a standardized 8-section report, we could guarantee that every financial number was calculated by deterministic code, using the LLM solely to summarize the findings.

The Build

Our architecture splits concerns between a high-performance mathematical engine and a fast web interface. We decoupled data calculations from text generation to prevent latency bottlenecks.

graph TD
    A[Next.js Frontend] --> B[FastAPI Backend]
    B --> C[Financial Data API]
    B --> E[LLM Narrative Generator]
    B --> D[Python Math Engine]
    A --> F[Supabase Database]

1. The Python Calculation Engine

AI models are notoriously bad at math. To ensure our valuation metrics were 100% accurate, we wrote the calculation engine in Python. When a user requests a report, the Python engine fetches raw financial data, computes historical multiples, runs a multi-stage DCF model, and parses technical indicators. The raw numbers are never generated by the LLM.

Running this in Python allowed us to leverage libraries like NumPy and Pandas, executing complex discounted cash flow projections in milliseconds.

2. The AI Narration Layer

Once the math is complete, the structured payload is passed to the LLM. The model's only job is to write the analysis. We feed it the precise numbers alongside a custom system prompt that enforces a neutral, professional tone. This prevents the "hallucination" of financial statistics while ensuring the output reads like a research note rather than a generic chat response.

3. Frontend and Persistence

The frontend is built with Next.js 14. To keep reports fast, we cache completed runs in Supabase. If a user requests a report for a stock that was analyzed earlier in the day, we serve the cached version instantly.

The Result

The completed MVP of briefstock.ai currently covers 4,500+ US stocks in production.

When a user requests an analysis, the platform delivers an 8-section report in under 60 seconds. These sections cover DCF Valuation, FCF Analysis, Risk Flags, Sentiment Scores, Technical Analysis, Peer Comparison, and an AI Verdict.

By offering a freemium model, we allowed retail investors to test the product before subscribing, driving strong organic growth.

Lessons Learned: Handling Financial API Rate Limits

Financial data APIs are expensive and carry strict rate limits. In our first build, we queried our data providers in real-time on every report request. This quickly triggered rate limit errors during peak trading hours.

We resolved this by building a caching database layer. We stored daily price feeds and quarterly balance sheet snapshots directly in Supabase. Our Python engine queries our internal Supabase database first, only fetching data from external APIs if the record is missing or outdated. This reduced our API bills by 80% and cut report generation times by half.

What This Means for Your Project

If your MVP requires complex data processing or AI integrations, do not try to build everything inside a single monolithic framework.

Decoupling your application is the key to scaling. By using Python for heavy data calculations and Next.js for the user interface, we built a highly performant application that costs almost nothing to run. We also cached our results to prevent repetitive API charges from third-party data providers.

When scoping your AI product, separate the math from the narration. Let code do the calculations and let AI do the writing.