Technology Stack

We are transparent about what we use and why. No black boxes, no hand-waving — documented architecture decisions backed by engineering rationale.

Layer by Layer

01

Data Ingestion

Protocol-agnostic ingestion supporting IoT sensors, PLCs, APIs, and legacy system adapters.

MQTTModbus TCPHTTP/WebSocketCustom Adapters
02

Stream Processing

Real-time event processing with guaranteed ordering, exactly-once semantics, and back-pressure handling.

Apache KafkaRedis StreamsCustom Event Pipeline
03

Data Storage

Time-series optimized storage for sensor data, relational databases for configuration and metadata, object storage for archives.

TimescaleDBPostgreSQLInfluxDBS3/Object Storage
04

Application Logic

Service-oriented backend with dedicated microservices for ingestion, processing, alerting, and API gateway.

Node.jsPythonRust (edge)Express/Fastify
05

Intelligence Engine

The analytical core — signal correlation, hypothesis generation, confidence scoring, and decision recommendation.

Custom Correlation AlgorithmsTemporal Pattern AnalysisRule EngineStatistical Models
06

Frontend & Dashboards

Operational dashboards with live data streams, interactive charts, and role-based access control.

React / Next.jsD3.jsWebSocket Real-Time UIResponsive Design
07

Cloud Infrastructure

Container-orchestrated deployment with infrastructure-as-code, auto-scaling, and multi-region capability.

AWS / GCPDockerKubernetesTerraform
08

Security

End-to-end encryption, token-based authentication, role-based access control, and audit logging.

TLS 1.3OAuth 2.0 / OIDCRBACEncryption at Rest (AES-256)

On AI & Machine Learning

We integrate AI/ML where it demonstrably outperforms rule-based approaches — and we're transparent about where it doesn't.

For most operational monitoring and automation tasks, well-engineered rule-based systems with statistical thresholds outperform black-box ML models. They're more explainable, more auditable, and more reliable in production.

We use machine learning for specific tasks where it provides clear value: temporal pattern recognition in high-dimensional sensor data, anomaly detection in noisy environments, and demand forecasting with complex seasonal patterns.

We don't label everything "AI-powered" because it sounds impressive. We label it what it is: engineering.

Real-Time Processing

Sub-second data ingestion to dashboard rendering. Event-driven architecture with stream processing ensures no data point is batched or delayed. Operators see what's happening now — not what happened 5 minutes ago.

Security Architecture

Defense in depth: TLS everywhere, token-based auth with short expiry, role-based access, encrypted storage, audit logging, and network segmentation. Security is a constraint in every architecture decision — not an afterthought.

Cloud Infrastructure

Infrastructure-as-code with Terraform, containerized services on Kubernetes, auto-scaling based on load metrics, multi-region for high availability. Every deployment is reproducible and version-controlled.