A rigorous pipeline — every stage auditable, every output reproducible. No black boxes, no discretionary overrides.
ARGOS continuously polls the SEC EDGAR full-text search and RSS feeds. New filings are detected within minutes of publication and queued for processing. The pipeline has continuous coverage going back to 2014 — every 10-K, 10-Q, and 8-K for 4,800+ liquid tickers.
Raw HTML/XBRL is stripped and the filing text is segmented into individual sentences. Legal boilerplate — forward-looking statement disclaimers, risk factor repetitions, exhibit lists — is identified and removed using Hyperscan pattern matching before any classifier ever sees the text. This removes 60%+ of low-signal content and concentrates classifier power on substantive disclosure.
Each sentence is embedded into 768-dimensional space (E5-base-v2) and scored by every classifier in parallel. 159 are human-curated from investment theses. 313 were discovered by unsupervised clustering on 151M sentences — finding signals humans would never think to look for. But scoring is only half the story. AUC alone doesn’t catch classifiers that fire on the wrong things.
AUC measures how well a classifier separates its training data. It says nothing about what happens on 151 million real sentences. We built three independent quality gates — each catches a different failure mode.
Cross-validated precision on held-out data. Can the classifier reliably separate its own training examples? If not, it hasn’t learned the concept.
Score 1M real sentences. If a classifier fires on more than a few percent, it’s detecting a topic, not an event. Specificity drives quality — rare signals are tighter signals.
Sample 1,000 high-confidence positives and measure whether they cluster in embedding space. A classifier can pass AUC and fire rate but still fire on unrelated sentences that share surface vocabulary. Coherence catches this.
Removing 62 incoherent classifiers (passed AUC, failed coherence) improved downstream model IC by +48%. They weren’t just useless — they were actively injecting noise. Removing bad signal beats adding good signal.
Browse the full classifier catalog →
Sentence-level scores are aggregated to the filing level — summed or max-pooled depending on the signal type. The result is a flat vector of classifier scores per accession number, ready to join to your pricing or fundamental data. Classifiers are frozen and immutable: the scores you backtest today will be identical to the scores you receive in production next year.
Once a classifier version ships, its weights and thresholds are locked. We never silently retrain or update deployed classifiers. If we improve a classifier, it ships as a new versioned column — the original remains unchanged.
Explore live classifier output from recent SEC filings — or request a full sample dataset for your backtest.