Deep Seer

The Problem

Intelligence lives in silos by design

The EPA publishes pollution data. The FAA tracks aircraft. The FEC discloses campaign contributions. The Coast Guard monitors vessels. The USGS records earthquakes. Each database exists in isolation — different formats, different APIs, different update schedules, different interfaces. By the time a journalist manually cross-references three of them, the situation has changed.

This fragmentation isn't accidental. It's structural. And it's the reason a factory can violate EPA permits for years while the health data from the community downwind lives in a completely different database that nobody thinks to check at the same time.

Existing platforms either embed iframes to different services (aggregation without integration) or charge enterprise prices that put them out of reach for the journalists, researchers, and NGOs who need them most. There was no platform that unified these sources into a common intelligence picture, applied peer-reviewed statistical methods to the live data, and produced evidence that could survive a courtroom.

By the Numbers

The Solution

Unified, not aggregated

Deep Seer normalizes every data source into a common entity model that shares three dimensions: geospatial (coordinates), temporal (timestamps), and relational (links between entities). This means you can cross-reference a ship with a congressional stock trade with an EPA violation — because they share a place and a time.

Everything renders on a single interactive 3D globe built on CesiumJS. 106 layers. Real-time streaming via WebSocket. Aircraft trajectories, vessel tracks, satellite orbits (SGP4 propagation running client-side), pollution facilities, weather systems, seismic events, campaign finance flows — all alive, all moving, all queryable.

The free tier includes all 90+ data sources, 61 real-time map layers, and 15,000+ CCTV traffic camera feeds. No credit card. No time limit. Intelligence tools shouldn't be gatekept by price.

Evidence Integrity

Built for the courtroom, not the screenshot

Every intelligence platform has screenshots. Deep Seer is built for legal proceedings. Every piece of evidence collected carries a SHA-256 fingerprint, an HMAC-SHA256 signature, and an RFC 3161 cryptographic timestamp. The chain of custody is logged immutably.

When a defense attorney challenges the authenticity of a satellite image or a social media capture, you don't argue — you provide the cryptographic proof. Export templates are pre-formatted for ICC Rome Statute, ECHR Rule 47, and US Federal Rules of Evidence 901/902.

Data lineage tracks every analytical output back to the raw API response. Full provenance. Full reproducibility. The difference between justice and impunity is often just the quality of evidence.

Scientific Analysis

Peer-reviewed methods, not vibes

Deep Seer implements the statistical methods that actual researchers publish in actual journals. Granger causality testing. Difference-in-Differences. Synthetic Control. Regression Discontinuity. Geographically Weighted Regression with Gaussian kernel weighting. Moran's I spatial autocorrelation. LISA cluster mapping. Holt-Winters + ARIMA ensemble forecasting with walk-forward backtesting.

Every statistical claim comes with p-values, confidence intervals, and methodology citations you can look up. An automated Bias Advisor flags selection bias, confirmation bias, base rate fallacy, and ecological fallacy before you publish a conclusion. The most dangerous findings are the ones that feel obvious. Deep Seer helps you check yourself before your critics do.

The Hard Parts

What made this difficult

• Normalizing 90+ heterogeneous data sources Every source has a different API, a different format, a different update frequency. 98 server adapters translate each one into a common entity model — shared geospatial, temporal, and relational dimensions. The normalization layer is the platform. Everything else is just rendering.
• 3D globe with 106 layers at interactive framerates CesiumJS handles the globe. But 106 simultaneously renderable layers — with particle animations for traffic flow, trajectory trails for aircraft, custom shaders for visualization modes (CRT, Night Vision, FLIR, Matrix) — required careful layer management, LOD strategies, and client-side SGP4 satellite propagation to keep the server load sane.
• Geolocation verification from photos Landmark pair matching uses Direct Linear Transform (DLT) homography with 4+ point calibration. Shadow chronolocation sweeps sun positions to determine image capture time. Building footprint comparison queries OpenStreetMap Overpass. All producing cryptographically signed analytical outputs.
• Downstream contamination modeling Gaussian plume transport from pollution point sources, integrated with CDC mortality data and EPA water quality monitoring, overlaid with Social Vulnerability Index mapping. Turning three disconnected datasets into a single visual argument for environmental justice.
• Privacy-preserving intelligence Face detection runs entirely in-browser — never transmitted to any server. Client-side metadata stripping before upload. AES-256-GCM encryption at rest. SecureDrop integration for source protection. Building an intelligence tool that treats privacy as architecture, not policy.

Architecture

How it's built

React 19 frontend with Vite 6. CesiumJS + Resium for the 3D globe. Zustand for state management across 70 stores. Express backend with 94 services. PostgreSQL with PostGIS for geographic queries. Prisma ORM across 35 database models. WebSocket for real-time streaming. Claude AI for document intelligence and entity extraction.

The investigation workflow follows the Berkeley Protocol's six-stage pipeline: Discovery, Collection, Preservation, Verification, Analysis, Reporting. It's not an afterthought — it's first-class. Protected site detection for humanitarian workflows. IHL violation analysis. DBSCAN incident pattern clustering. Entity behavioral profiling with intent classification and next-action prediction.

Deployed on Render via two-stage Docker build (Node 20 Alpine). CI/CD through GitHub Actions with type checking, unit tests, and E2E tests. Sentry for error tracking. Stripe for billing. The monorepo uses npm workspaces.

Philosophy

Democratize access to what's already public

Every data source in Deep Seer is public. Published by governments and institutions about themselves. The EPA publishes its own violation data. The FEC publishes its own donation records. The FAA publishes its own flight data. This isn't surveillance — it's accountability. The information exists. The question is whether it's accessible to the people who need it.

Most intelligence platforms price out the journalists, NGOs, and researchers who would use them for public interest work. Deep Seer's free tier includes all 90+ data sources because the people doing accountability work shouldn't have to outbid the people they're investigating.

The world isn't going to watch itself. Every minute you don't have situational awareness is a minute someone else controls the narrative. Deep Seer is live. The data is streaming. The only question is whether you're watching.