When we set out to build Sovarion's 30-day research reports, we had a simple question: what would it take to produce something indistinguishable from a JP Morgan equity research note — but generated by AI in 90 seconds instead of by a human analyst in a week?
The answer turned out to be a 7-module pipeline that we call the Brain. Each module has a specific job, and the output of one feeds into the next. There's no magic — just a lot of data, a lot of structure, and a very opinionated AI.
Module 1: Data Ingestion. The Brain starts by collecting everything it can find. SEC 10-K and 10-Q filings give us the actual income statement: revenue, gross profit, operating income, net income, EPS, R&D spend, shares outstanding. Finnhub provides analyst consensus (buy/hold/sell distribution with price targets), insider transaction history (who's buying, who's selling), EPS and revenue estimates for the next 4 quarters, earnings surprise history, social sentiment from Reddit and Twitter, and fundamental metrics. We also pull daily price candles for 200 trading days, news from 35+ sources, and the current VIX level.
Module 2: Context Analysis. Raw data is useless without context. The Context Layer classifies the market regime (trending, ranging, event-driven, risk-off, etc.), measures volatility, volume, and trend quality. For 7-day and 30-day analysis, we skip the low-liquidity override — weekend volume is irrelevant for a monthly thesis.
Module 3: Memory. The Brain remembers. It retrieves historical analogs — past situations with similar market conditions — and checks how those played out. It also maintains a behavioral profile per asset: how sensitive is Tesla to news? How reliable are Apple's trend signals? These profiles update automatically through a feedback loop.
Module 4: Hypothesis Generation. This is where Claude AI enters. Given all the structured data, Claude generates 2-4 competing investment hypotheses. For 30-day reports, the prompt explicitly tells Claude to "think like a senior equity research analyst at JP Morgan." It must take a directional stance, cite specific numbers from the data, and provide evidence for and against each hypothesis.
Module 5: Adversarial Critic. Every hypothesis is challenged by a second Claude call. The critic looks for weak evidence, overconfidence, and contradictions. It assigns a confidence penalty to each hypothesis. This is how we prevent the AI from being blindly bullish or bearish.
Module 6: Scoring. A purely mathematical layer (no AI) calculates 6 quality scores: Confidence, Signal Quality, Context Fit, Fragility, Risk Load, and Pattern Reliability. These scores determine the final decision class: aggressive bullish, controlled bullish, neutral, controlled bearish, or aggressive bearish.
Module 7: Report Generation. The final Claude call takes everything — the winning hypothesis, all supplementary data, and the scoring results — and writes the full research note. For 30-day reports, this means 8 sections: Executive Summary, Macro Backdrop, Fundamental Analysis (with peer P/E comparison and EPS trajectory), Technical Picture, Catalysts & Timeline, Risk Factors, Insider & Institutional Signals, and Analyst Consensus. Plus three-scenario price targets with mathematical justification.
The result is a 3+ page document that references actual P/E ratios ("31.9x vs peer DELL at 18.9x"), real earnings data ("beat in 3 of 4 quarters by avg 3.3%"), and specific price levels. It's not a generic summary — it's a research note you could bring to a client meeting.
The entire process takes about 90 seconds per asset. We generate reports for all 10 assets every Sunday evening, so they're fresh for Monday morning. Total cost: approximately $5 per month for all 30-day reports combined.
Is it as good as a human analyst who spent a week on a single stock? No. But it's 95% as good, covers 10 assets simultaneously, and costs a fraction of what institutional research typically costs. That's the trade-off we're making — and we think it's worth it.