Data, Analytics, and AI/ML
Transform raw streaming and batch data into strategic business intelligence with enterprise-grade solutions
IoT environments generate enormous and diverse data streams from sensors, devices, and machines—each with unique formats, frequencies, and complexities. Harnessing the full potential of this data requires specialized data engineering, advanced analytics, and AI/ML solutions built for scale and speed.
Thingularity offers end-to-end data engineering, analytics, and AI/ML services tailored for IoT, combining robust data pipelines, scalable architectures, and intelligence. We empower organizations to seamlessly collect, process, and analyze IoT data—transforming raw information into real-time insights, predictive models, and smarter business outcomes, while ensuring security, reliability, and integration within the broader IoT ecosystem.
Services
Data Pipeline Architecture
- Design and implement robust ETL/ELT pipelines with cloud-native orchestration tools.
- Real-time and batch processing capabilities with automatic scaling, error handling, and data quality monitoring for mission-critical workloads.
Data Warehouse & Data Lake Solutions
- Build scalable data warehouses and implement Lakehouse with Kappa Architecture (Unified Batch & Stream).
- Optimized storage strategies, partitioning schemes, and query performance tuning for huge analytics workloads.
Real-time Stream Processing
- Implement high-throughput streaming analytics with cloud stream processing services.
- Enable real-time anomaly detection, complex event processing, and low-latency data transformation for time-sensitive business decisions.
BI Reporting & Analytics
- Create interactive dashboards and automated reporting solutions visualization frameworks.
- Self-service analytics platforms with role-based access control and embedded analytics capabilities.
- Performance analysis -> tracking key metrics like average Temperature of a building or moving standard deviation of Humidity over time and energy consumption of a motor.
- Trend analysis -> identifying patterns and changes over time.
- Comparative analysis -> comparing different data points or segments.
- Root cause analysis -> investigating the underlying reasons for issues.
- 2D / 3D Digital Assets -> Like Real time visualization of average temperature of Building floors using color code superimposed on a 2D architecture plan of a building or 3D diagram.
AI/ML Services
- Predictive Analytics: Using historical data to forecast future outcomes.
- Prescriptive Analytics: Determines “what should we do next?” by combining insights from previous analyses. Utilizes AI and machine learning to recommend actions and solutions. Examples: Optimizing voltage control of an equipment based on external sensor values.
- AI Models : Neural Network based time series forecasting , pattern recognition , neural based anomaly detector
- Real time AI-ML pipeline : Feature Engineering , Model Training , MLOps
- Predictive Maintenance & Remaining Useful Life : Sensor data (vibration, temperature, pressure, etc.) are processed in real time to detect anomalies and compute predictive features. ML models estimate Remaining Useful Life (RUL), enabling proactive maintenance and reducing downtime, with results visualized on live dashboards.
- Predictive Intelligence for Process Optimization : Leverages advanced capabilities including anomaly detection, predictive modeling, reinforcement learning, and digital twin simulations to provide deep insights into operational health. The system anticipates potential equipment failures, flags inefficiencies, and helps fine-tune processes—minimizing downtime and material waste while improving overall reliability and throughput.
- Smarter & Adaptive ML at Scale : Continuously updates models through real-time feature engineering and online learning loops. Ensures consistency between training and inference pipelines to eliminate model drift—ideal for dynamic use cases (FinTech) like adaptive fraud detection, credit risk evaluation, and event-driven compliance monitoring.