Technology & Architecture

Built on cutting-edge AI research and enterprise-grade infrastructure.

Architecture Overview

Predictir AI's platform is built on a modern, scalable architecture designed for high performance, reliability, and security. Our microservices-based system enables independent scaling of components, ensuring optimal resource utilization and minimal latency.

Machine Learning Models

Time Series Forecasting

Our time series models leverage state-of-the-art architectures including:

LSTM & GRU Networks: Deep recurrent neural networks for capturing long-term dependencies and complex temporal patterns
Transformer-based Models: Attention mechanisms for modeling relationships across different time horizons
Prophet: Facebook's forecasting tool for handling seasonality, holidays, and trend changes
ARIMA Variants: Classical statistical models (ARIMA, SARIMA, ARIMAX) for interpretable forecasts
Ensemble Methods: Combining multiple models to maximize accuracy and robustness

Graph Neural Networks

For customer intelligence and relationship modeling, we employ graph neural networks (GNNs) that capture complex relationships between entities:

Graph Convolutional Networks (GCN): For node classification and link prediction in customer networks
Graph Attention Networks (GAT): Attention-based mechanisms for learning importance weights in relationships
GraphSAGE: Inductive learning for large-scale graphs with dynamic node additions

Computer Vision

Our vision models are built on proven architectures optimized for production:

ResNet & EfficientNet: Deep convolutional networks for image classification and feature extraction
Vision Transformers (ViT): Transformer-based models for image understanding
YOLO & Faster R-CNN: Real-time object detection and localization
Custom Architectures: Domain-specific models trained on client data for specialized use cases

Probabilistic Inference

For uncertainty quantification and explainable predictions, we use probabilistic models:

Bayesian Neural Networks: Providing uncertainty estimates alongside predictions
Gaussian Processes: Non-parametric models for time series with uncertainty bounds
Monte Carlo Methods: Sampling-based inference for complex probabilistic models

Security & Compliance

Data Privacy

End-to-end encryption (AES-256), data anonymization, and privacy-preserving machine learning techniques ensure sensitive data remains protected.

Access Control

Role-based access control (RBAC), multi-factor authentication (MFA), and fine-grained permissions for secure data access.

Compliance Certifications

SOC 2 Type II, ISO 27001, GDPR, CCPA, HIPAA (for healthcare deployments), and industry-specific compliance standards.

Audit Logging

Comprehensive audit trails for all data access, model training, and prediction requests for compliance and security monitoring.

Data Residency

Support for data residency requirements with region-specific deployments and data localization options.

Explainability

Model interpretability tools, feature importance analysis, and transparent prediction explanations for regulatory compliance.

Infrastructure & Performance

Cloud-Native: Built on Kubernetes for auto-scaling, high availability, and multi-cloud deployment

Performance: Sub-second inference for single predictions, batch processing for thousands of series in minutes

Scalability: Horizontally scalable architecture handling billions of data points and millions of predictions per day

Reliability: 99.9% uptime SLA with redundant systems, automated failover, and disaster recovery

Deployment Options: Public cloud (AWS, Azure, GCP), private cloud, on-premises, and hybrid configurations