Atlas4D Investor Brief
from single-city pilot to regional 4D intelligence layer

Atlas4D is a 4D spatiotemporal data layer that unifies GIS, time-series, network telemetry, events and AI into one real-time "brain" for cities, networks and critical infrastructure.

📈 Current Traction & Technical Validation

Atlas4D is not a slide deck – it is a running production system with real services, real data and real observability.

13,000+
Network observations
Processed and stored in the live deployment.
30+
Microservices & workers
Core API, AI, ingestion, fusion and observability services.
γ = 0.996
MALP calibration
Excellent concordance for event attendance predictions.
225×
Rendering improvement
MVT/PMTiles vs legacy GeoJSON for large point clouds.

🏗️ Microservice Architecture (grouped)

Core & Access
  • API gateway and public API
  • Core domain backend (entities, events, observations)
  • Auth service with JWT
  • Frontend (MapLibre GL + UI)
Data & Sensors
  • Weather ingestion & API (current + forecast)
  • Radar / edge processors (tracks, quality)
  • IoT & network observations
Intelligence Layer
  • Event risk prediction with MALP calibration
  • Anomaly detection & multi-sensor fusion
  • Threat forecasting & trajectory prediction
  • NLQ + STSQL + embeddings (Ollama + pgvector)
Observability
  • Prometheus metrics (core services)
  • Grafana dashboards
  • Exporters for network, DB and Nginx
  • Automated health checks & alerts

🚀 Key Modules in Production Today

🛡️
Network Guardian

Real-time network monitoring with ping/SNMP workers, semantic search (pgvector + LLM), H3-indexed observations and MVT visualization. Currently processing 13,000+ observations.

⚠️
Event Risk with MALP

Attendance prediction with Maximum Adjusted Linear Prediction (γ = 0.996), combining weather, calendar context and historical behaviour with transparent bias correction.

🔮
Threat Forecasting

Fusion of trajectories, anomalies and environmental conditions into short-term risk forecasts on an H3 grid, with a configurable prediction horizon.

✅ Deployment & Operations

Infrastructure
  • On-premises Docker deployment
  • 30+ containerized services
  • Nginx reverse proxy with TLS
  • Automated health checks & restarts
Data & Security
  • PostgreSQL + PostGIS + TimescaleDB
  • JWT-based authentication
  • Role-aware APIs and rate limiting
  • Regular backups and retention policies
Observability
  • Prometheus metrics for core services
  • Grafana dashboards for system health
  • Exporters for database, Nginx and host
  • Alerting hooks ready for ops integration

🎯 Bottom line: Atlas4D is a live, observable system – not a prototype. The architecture, data model and deployment are already in place; funding now goes into productization and scaling, not into “building from scratch”.

The problem Atlas4D solves

Modern cities are blind in 4D: space + time + movement.

Fragmented sensors

Sensor chaos and data silos

  • Radar, cameras, IoT, weather, network tools all speak different languages.
  • Each vendor ships its own closed dashboard and proprietary format.
  • No single view of “what is happening right now” across all layers.
No unified 4D model

From static maps to moving reality

  • Most systems can answer “where is it now?”, but not “how did it get here?”.
  • History is overwritten instead of versioned in time.
  • Cities lack a digital twin of movement: drones, vehicles, people, packets.
No prediction layer

Reactive instead of predictive

  • Threats, failures and crowd risks are discovered too late.
  • No way to ask: “what will happen in the next 30 minutes?” on the map.
  • Operators jump between tools instead of acting on one risk picture.

📌 Positioning

Atlas4D is not just a dashboard – it is a 4D spatiotemporal data layer for the real world.

🏷️

Category

“4D Spatio-Temporal RDBMS / Data Platform”
A database-backed platform that models entities, movement, events and risk across space and time on top of PostgreSQL.

🧠

Core Idea

“A database that mirrors the real world in space and time.”
Atlas4D unifies GIS + time-series + events + graph-like relations + vector AI into one spatiotemporal model, exposed via SQL, STSQL, APIs and maps.

💡

Unique Angle

While others sell separate GIS tools, monitoring systems or AI add-ons, Atlas4D is built as a native 4D layer: time, space, trajectories, anomalies and predictions are first-class citizens, not afterthoughts or plugins.

🌍

Long-Term Vision

Atlas4D has the potential to become a “Linux for spatiotemporal data”: an open, extensible layer that cities, telcos and infrastructure operators can run on-prem or in the cloud, with SDKs for Rust / Go / Python and a growing ecosystem of domain-specific modules (NetGuard, Event Risk, Threat Forecasting, etc.).

What is already built and running

Atlas4D is not a slideware idea. It is a running platform with real services.

Core platform

Unified 4D data layer

  • PostgreSQL + TimescaleDB + PostGIS + H3 + pgvector, fully integrated.
  • Single observations_core schema for radar, weather, IoT, network.
  • STSQL extension for time+space queries: DURING, NEAR, WITHIN, PREDICT.
Microservices

22+ production-grade services

  • Frontend, API Gateway, Auth, Public API.
  • Weather, Radar Edge, IoT ingestion, Fusion & Fusion Coordinator.
  • NLQ, Vision (GPU), Anomaly & Anomaly Fusion, Threat Forecaster.
Network Guardian v1.0

AI-assisted network monitoring

  • SNMP + ping workers with TimescaleDB hypertables.
  • Geospatial enrichment via H3/PostGIS and MapLibre GL dashboard.
  • Semantic search with pgvector + embeddings (“devices with high latency near Burgas”).
Event Risk & MALP

Bias-corrected event attendance forecasting

  • Event prediction microservice with Maximum Adjusted Linear Prediction (MALP).
  • Weather, calendar and context features, with transparent calibration and CCC metrics.
  • REST API + dedicated UI for event risk assessment.
Explainable AI

Causal fusion & threat forecasting

  • Causal rules engine that fuses anomalies from radar, weather and other sensors.
  • Per-threat explanation in human language with confidence scores.
  • 30-minute threat forecasting combining anomalies, weather hazards and trajectories.
Developer-ready

Modern, observable architecture

  • Docker Compose deployment with 22+ services and health checks.
  • Prometheus, Grafana, Loki, Tempo for metrics, logs and traces.
  • Private GitHub repository with full history and documentation.

👤 Team

Atlas4D is founder-built by someone who has lived in networks, infrastructure and real-time systems for years.

Founder

[Hristo Beyazov&Digicom team] – engineers and architects with hands-on experience in telecom, network monitoring and geospatial systems.

Has designed and operated production systems for critical infrastructure and networks, where downtime and blind spots are not an option. Atlas4D is a synthesis of this experience: “from siloed monitoring tools to one 4D model of reality”.

Currently building Atlas4D full-time, bootstrapped, with a strong bias towards running code over slides.

Roadmap & funding scenarios

What happens if you put capital behind a working prototype.

Scenario A · Pilot city

€80k–€120k · 9 months

Goal: Fully packaged Atlas4D deployment for one mid-size city / operator.

  • Hardening of existing services, security review, backup & disaster recovery.
  • Productization of Network Guardian and Event Risk as two clear offerings.
  • Installer / automation scripts for on-prem or private cloud.
  • Reference deployment and case-study for 1 real customer.

Result: from prototype to sellable pilot with live reference.

Scenario B · Multi-tenant SaaS

€250k–€350k · 18 months

Goal: Multi-tenant Atlas4D platform for multiple cities / enterprises.

  • Multi-tenant architecture, RBAC, customer isolation and billing integration.
  • Self-service onboarding, API keys, quotas and per-tenant dashboards.
  • Scaling of AI services (GPU pooling, background workers, queues).
  • Two additional verticals: smart city mobility + critical infrastructure.

Result: SaaS-ready platform with repeatable revenue model.

Scenario C · Regional play

€750k–€1M · 24–30 months

Goal: Regional spatiotemporal intelligence provider (country / region scale).

  • Clusters across multiple regions, high-availability and autoscaling.
  • Connectors to national systems (aviation, emergency, transport, telco).
  • Dedicated ML research on trajectory patterns, threats and crowd dynamics.
  • Sales & support team for EU markets, certifications and compliance.

Result: category-defining platform for 4D intelligence in the region.

All numbers above are indicative and can be adapted to specific investor strategy, timelines and geography.

💶 Funding & Use of Proceeds

Atlas4D is raising a €650K seed round (target range €600K–700K) to fund 18 months of runway, 2–3 core hires and 2–3 lighthouse pilots with cities, telcos or infrastructure operators.

👥 Team (people)

~€300K – core engineering team.

  • Founder: ~€4.5K / month (18 months)
  • Senior backend / infra engineer
  • ML / AI engineer (forecasting, embeddings, STSQL)
  • Full-stack / product engineer (around months 6–7)

🛠️ Product, Infra & Pilots

~€140K – to turn Atlas4D into a repeatable product with 2–3 lighthouse deployments.

  • On-prem / edge hardware for pilots (radar, IoT, network)
  • Cloud & data infrastructure, backup and observability
  • Integration and on-site setup for early customers

🚀 GTM, Legal & Ops

~€110K – go-to-market and operational backbone.

  • Conferences, demos and sales materials
  • Legal, IP, contracts and accounting
  • BD / sales support for lighthouse pilots

🧊 Strategic Buffer

~€100K – reserved for uncertainties in deep-tech and on-prem deployments.

  • Delays in pilots or hardware lead times
  • Additional engineering / contractor support if needed
  • Runway protection for on-prem / edge scenarios

🎯 What this round unlocks (18-month plan)

  • Team: founder + 2–3 core engineers fully focused on Atlas4D.
  • Product: hardening Atlas4D into a deployable “4D reality layer” with clear APIs and STSQL / NLQ interfaces.
  • Pilots: 2–3 lighthouse deployments (city / telco / infrastructure operator) with live data.
  • Proof: production-grade observability, real customers and a clear path from platform to repeatable product.

Interested in Atlas4D as an investment?

There is already a running platform. The next step is to turn it into a product line.

⏱️ Why Atlas4D, why now

The timing for a 4D spatiotemporal data layer is uniquely good.

Macro trends

  • Explosion of sensors: drones, IoT, cameras, 5G, edge devices
  • Regulators push for better airspace & infrastructure awareness
  • Cities and operators are expected to react in real time, not after the fact

Technical inflection

  • GPU/AI tooling makes real-time fusion feasible outside big tech
  • PostgreSQL ecosystem (PostGIS, TimescaleDB, pgvector, H3) is mature
  • Open-source stacks let a small, focused team move very fast

Business model

  • Annual licenses for cities, telcos and infrastructure operators
  • Cloud SaaS for mid-size customers (multi-tenant Atlas4D)
  • Optional professional services for integrations and custom models

💶 Stage & funding

Atlas4D is pre-seed / seed stage, founder-built and already running in production. We are exploring a funding round to productize the platform with 1–2 lighthouse customers.

Use of funds (high-level)

  • Deliver 1–2 full pilots (city / telco / infrastructure operator)
  • Harden the SaaS platform (observability, multi-tenant, self-service)
  • Expand SDK ecosystem (Rust / Go / Python clients, templates)
  • Initial go-to-market for cities, telcos and critical infrastructure operators