Investment-ready spatiotemporal intelligence platform
Atlas4D turns raw sensor chaos (radar, IoT, weather, networks, events) into one real-time "4D brain" that understands, predicts and helps you act in space and time.
Atlas4D is a working deep-tech platform – not a slide deck. Built to scale from a single city to an entire country.
Critical infrastructure, airspace and networks generate massive spatiotemporal data, but tools are siloed: radar, weather, IoT, video, events, network monitoring – all live in separate systems with no unified “4D brain”.
A unified 4D fabric (space + time) that ingests radar, weather, IoT, vision, events and network metrics into one model, with STSQL, NLQ and AI services on top – ready for real-time decisions.
22+ microservices, live radar & weather fusion, threat forecasting, Network Guardian, event risk module with MALP calibration, full observability (Prometheus + Grafana) and running infrastructure.
Public safety & airspace security, telco & network operations, smart city command centers, large event operators and critical infrastructure providers.
Deep database + geospatial architecture, 4D data model, multi-sensor fusion engine, and domain-specific AI modules built on open standards (PostGIS, H3, Timescale, pgvector).
1–2 lighthouse pilots with a city, telco or infrastructure partner, hardened multi-tenant cloud version,
and regional rollout across SEE / EU.
Stage: pre-seed / seed, founder-led, bootstrapped.
"Atlas4D doesn't log events-it logs the history of movement."
Static data
Temporal data
Predictive analytics
We work with history, not just "state". No overwrites-versioned intervals (valid_from → valid_to).
STSQL: time + space + motion. Example: "Which drones approached the airport in the last 30 minutes?"
Trajectory analysis, vectors, and predicting positions N minutes ahead. Alerts before the event.
Dozens of specialized services-independent yet coordinated (IoT, AI, Weather, Vision).
Idempotency, Snowflake ID, dual-layer auth. Resilient to restarts and network loss.
Digital twin of reality with LLM/NLQ, vector trajectories and predictive models.
Atlas4D is a 4D spatiotemporal data layer – a database-backed platform that mirrors the real world across space and time.
Instead of separate systems for GIS, time-series, events and logs, Atlas4D keeps movement in space and time in one model – entities, trajectories, anomalies, threats and events.
Built on PostgreSQL + PostGIS + TimescaleDB + H3 + pgvector, Atlas4D behaves like a 4D RDBMS: SQL-compatible, observable and production-ready, but with native support for geospatial, temporal and vector AI.
Radar, weather, IoT, video, network telemetry and events are ingested into a single spatiotemporal model – then exposed via maps, APIs, STSQL and NLQ for cities, telcos and infrastructure operators.
Atlas4D is designed as an infrastructure layer: it runs on your hardware, next to your sensors and existing systems, and complements your current monitoring, GIS and analytics stack instead of replacing it overnight.
Our spatiotemporal extension for real-time queries
-- Drones near the airport in the last 30 minutes
SELECT *
DURING ["now-30m","now"]
NEAR POINT(42.6977,23.3219)
WITHIN 5km
LIMIT 100;-- Predict position in 10 minutes and check a restricted zone
SELECT entity_id, predicted_position
FROM trajectories
WHERE PREDICT(+10m)
AND INTERSECTS(restricted_zone);Everything for spatiotemporal data analytics
PostGIS, H3 indexes and STSQL for massive real-time spatial data.
TimescaleDB continuous aggregates and materializations for fast dashboards and alerts.
MapLibre GL + MVT/PMTiles-millions of points, smooth render without blocking.
Integration with Ollama LLM: multilingual NLQ, STSQL generation, trajectory forecasts.
IoT, Vision, Radar, Weather, Fusion… each service is a domain expert.
Anomaly detection and predictive alerts with fusion and ML.
Real-time geospatial analytics and visualization
Vehicle positioning with MVT tiles
MapLibre GL with Bulgaria coverage
Filters by time, type, severity and location
H3 hexagonal binning with real-time data
Specialized services working in sync
MapLibre GL + React interface
Routing, auth, rate limiting
JWT authentication
External data ingestion
IoT device processing
Camera feed integration
Natural Language → STSQL
AI video/images
Vectorization
Forecasts
Pattern-based
Correlation
Predictive analysis
Realtime radar
Audio analytics
Orchestration
Merging
H3-indexed
Integrations
WebAssembly
ML inference
Legacy bridge
AI-powered risk assessment combining LSTM trajectory predictions, weather hazards, and anomaly patterns
Trajectory risk score
20 trajectories analyzed
Wind & storm risk
Real-time conditions
~0.1 km² cells
174m edge length
Prediction horizon
Configurable per request
Score: 0.0 - 0.3
Score: 0.3 - 0.7
Score: 0.7 - 1.0
POST /api/threats/forecast// Request { "lat": 42.5048, "lon": 27.4626, "horizon_minutes": 30 } // Response { "threat_level": "medium", "threat_score": 0.313, "trajectory_score": 0.897, "weather_score": 0.255, "confidence": 1.0 }
Real-time network infrastructure monitoring with geospatial intelligence and semantic search capabilities
┌─ Network Devices │ └─ SNMP/Ping Workers │ ├─ ping_worker (30s intervals) │ └─ snmp_worker (5min intervals) │ └─ Template-based collection │ ├─ Data Layer │ ├─ TimescaleDB (metrics hypertables) │ ├─ PostGIS (geospatial geometries) │ ├─ H3 (hexagonal indexing) │ └─ pgvector (embeddings storage) │ ├─ Processing Layer │ ├─ Ollama (embeddings generation) │ ├─ Alert Worker (threshold monitoring) │ └─ STSQL Adapter (query compilation) │ └─ Presentation Layer ├─ MVT Tiles (MapLibre GL) ├─ REST API (FastAPI) ├─ Prometheus Metrics └─ Web Dashboard
GET /api/netguard/search
// Query devices with performance issues near Burgas { "query": "network devices with high latency in Burgas region", "location": { "lat": 42.5048, "lon": 27.4626 }, "radius_km": 10, "time_range": "last_24h" } // Response with hybrid semantic + spatial + temporal results { "results": [ { "device_id": "lb.digicom.bg", "semantic_score": 0.89, "avg_latency_ms": 145.7, "distance_km": 2.3, "alert_count": 3 } ] }
AI-powered attendance prediction with Maximum Adjusted Linear Prediction (MALP) calibration for bias-corrected forecasts
1. Base Prediction (Heuristic Model) └─ Multi-factor analysis → base_prediction: 4500 2. MALP Calibration (Bias Correction) ├─ γ coefficient: 0.9962 ├─ Formula: calibrated = γ × base_prediction └─ Result: 4390 (adjustment: -110 or -2.4%) 3. Risk Assessment ├─ Capacity utilization: 4390 / 5000 = 88% ├─ Weather risk: precipitation probability ├─ Traffic risk: real-time observations └─ Combined risk score: 0.3 (Medium) 4. Response with Transparency ├─ predicted_attendance: 4390 ├─ base_prediction: 4500 ├─ calibration_method: "MALP" ├─ adjustment: -110 └─ confidence: 0.65 Benefits: ✓ Reduced bias → more accurate predictions ✓ Conservative estimates → better planning ✓ Full transparency → trustworthy results
Plan staffing, security, and resources with bias-corrected attendance forecasts
Optimize capacity, pricing, and logistics with MALP-calibrated predictions
Coordinate transport and emergency services with accurate crowd size estimates
Access full transparency: base vs calibrated predictions with adjustment deltas
POST /api/events/risk/assess
// Request - predict with MALP calibration { "event_name": "Summer Music Festival", "event_type": "concert", "venue_capacity": 5000, "location": { "city": "Burgas", "lat": 42.5048, "lon": 27.4626 }, "date": "2025-07-15T20:00:00Z" } // Response - MALP transparency included { "success": true, "predicted_attendance": 4390, "base_prediction": 4500, "calibration_method": "MALP", "adjustment_delta": -110, "adjustment_pct": -2.4, "risk_assessment": { "overall_risk": "Среден", "risk_score": 0.3, "attendance_risk": 0.88, "weather_risk": 0.15 }, "confidence": 0.65 }
GET /malp/stats
// Response - calibrator status { "calibrated": true, "n_events": 10, "gamma": 0.9961950734747499, "ccc_before": 0.9855, "ccc_after": 0.9733, "ccc_improvement_pct": -1.24, "data_source": "production", "last_calibration": "2025-11-11T14:20:22" } // Training endpoint POST /malp/train?force=false // Single prediction calibration POST /malp/calibrate?prediction=7500
Short version of the Atlas4D investor story: problem → what’s built → where we’re going.
Cities, telcos and infrastructure operators drown in spatiotemporal chaos: radar, weather, IoT, cameras, events, network monitoring – all in separate systems, with no unified 4D view and no "brain" that can predict what comes next.
Decisions are made on Excel, screenshots and phone calls, not on a live model of reality.
Atlas4D is founder-built, bootstrapped and deployment-proven. Next step: productizing the 4D data layer for 1–2 lighthouse customers.
We’re exploring a pre-seed / seed round to:
👉 Full investor brief: atlas4d.tech/investors-brief
The best for maximum performance
Recent development milestones from Sprint 1–13
Context-aware follow-up queries. Ask "What's the weather in Burgas?" then "And in Sofia?" – it remembers.
Redis intent cache, Ollama pre-warming, weather fast-path. From 21s to 70ms for common queries.
Unified header across all pages with hamburger menu. 7 navigation items: Dashboard, NLQ, Threats, STSQL, NetGuard, Events, Ops.
Optimized spatial scoring and threshold-aware processing. Real-time threat zone rendering at 0.1ms.
13k+ observations, SNMP/Ping workers, 11 ML models per device, MVT tiles, semantic search with pgvector.
AI attendance prediction with bias-corrected MALP calibration. Weather, traffic, and capacity risk scoring.
From safety to smart cities and industry
Drone incursions, anomalous trajectories, predictive alerts.
Airspace, traffic, coordinated incident response.
Load forecasts, coverage optimization.
Tracking, predictive maintenance, logistics.
Routes, collision avoidance, optimal trajectories.
Massive streams, anomalies, smart notifications.
Temporal monitoring and change analysis.
H3 grid, Open-Meteo integrations and correlations.
Atlas4D is already running as a working deep-tech prototype. Looking for investors and strategic partners for the first large-scale deployments.