Detect when documents
change meaning over time
A general-purpose semantic drift detection engine for large versioned document corpora. Tracking high-dimensional cluster trajectories, Wasserstein distributional shifts, and evidence-grounded materiality across 10 years of SEC 10-K disclosures.
bge-small-en-v1.5 with L2 normalization.Interactive Latent Space Trajectory (2016β2025)
Real-time UMAP projection of sentence embeddings across 10 fiscal disclosure years. Drag the slider to observe multi-modal cluster drift.
Forensic Architecture Pipeline
Interactive view of our modular document-to-drift analysis pipeline. Click any stage for engineering details.
bge-small-en-v1.5 representations with L2 normalization and disk caching.Enforces an 8 req/s monotonic rate limit to respect data.sec.gov rules. Pulls raw 10-K primary documents into immutable bronze folders with SHA-verified metadata sidecars across 10 fiscal years.
Interactive Knowledge Graph Network
Discover hidden cross-corporate dependencies and systemic contagion across 10 years of SEC disclosures. Click or hover any node to inspect connected filers, drift intensities, and verbatim excerpt diffs.
AI Infrastructure & Compute
Hyperscalers and device makers are simultaneously disclosing severe dependencies on frontier foundation models and custom accelerator clusters, creating an industry-wide compute chokepoint.
1. Spot Systemic Contagion
Filers sharing the same risk node face correlated operational vulnerabilities (e.g. NVIDIA, Apple, and Broadcom sharing Taiwan packaging dependencies).
2. Identify Critical Risk Hubs
Larger nodes represent high-centrality disclosure manifolds that bridge multiple distinct industry sectors.
3. Jump to Verbatim Evidence
Click any node or link in the inspector to directly open the side-by-side Before/After 10-K diffs grounding that connection.
Top Disclosed Drift Events (2016β2025)
Ranked by Materiality Score with Wasserstein distribution divergence. Click any row for forensic before/after verification.
| Entity | Theme Cluster | Year | Classification | Ξ Intensity | Centroid Drift | Wasserstein (p-val) | Materiality Score |
|---|
Company 10-Year Trajectory
Longitudinal evolution of thematic risk disclosures (2016β2025).
DuckDB-Wasm Interactive Sandbox
Execute real SQL queries directly on 10-year analytical Parquet files in your browser. Zero backend, zero server latency.
About DriftLens
A general-purpose semantic drift detection engine designed to detect when the meaning of recurring sections in versioned document corpora changes over time. Demonstrated on 10 years of SEC 10-K filings across 50+ leading companies.
What DriftLens Is
A document intelligence system combining bi-encoder embeddings, unsupervised manifold clustering, Wasserstein distribution distance, and local LLM evidence grounding to identify substantive corporate disclosure changes.
What DriftLens Is NOT
It is not a stock picker, trading bot, or generic RAG demo. SEC filings were chosen because they are legally standardized, versioned annually, and freely accessible β serving as the ideal testbed for semantic drift.
Permanent $0/Month Architecture
All machine learning and LLM inference runs offline once during batch ETL. The live web application executes entirely client-side via DuckDB-Wasm reading compressed static Parquet tables. Zero server bills forever.
End-to-End Latent Vector Forensics Architecture
How raw SEC Form 10-K disclosures are ingested, parsed into semantic representations, clustered into manifold themes, and served client-side with zero server cost.
Mathematical & Algorithmic Formulation
1. Calibrated Materiality Score:
Weights the magnitude of percentage shift by the depth of actual paragraph discussion, ensuring isolated 1-sentence tweaks do not outrank multi-page risk shifts.
2. Multi-Modal Wasserstein Distribution Distance:
Measures the true geometric transport distance between year N and year N-1 disclosure embedding clouds, verified via a 60-iteration permutation significance test (p < 0.05).
3. What You Can Do With This Engine:
- Audit Longitudinal Risk Trajectories: Track how a company's cyber or supply chain stance shifted across 10 years.
- Detect Novel Black Swans: Flag unprecedented outlier paragraphs before they become industry-wide clusters.
- Run In-Browser SQL Forensics: Execute ad-hoc analytical queries across all gold tables with DuckDB-Wasm.
- Verify Primary Evidence: Inspect side-by-side excerpt diffs grounding every synthesized claim in verbatim text.