Cloud-Native Architecture

System Architecture

A microservices-based, cloud-native platform engineered for infinite scalability, high availability, and enterprise-grade performance.

Overview

Architecture Overview

Sarthi DMS is built on a modern microservices architecture that ensures each component can scale independently while maintaining system resilience.

Microservices

Each capability is an independently deployable service — OCR, workflow, search, storage — allowing teams to develop, deploy, and scale each module autonomously.

Cloud-Native

Designed for containerized deployment on Kubernetes with support for AWS, Azure, Google Cloud, and on-premise environments using Docker and Helm charts.

Scalable

Horizontal auto-scaling at every layer — from API gateways to database clusters — ensures consistent performance from 100 to 10 million+ users.

Layered Design

Architecture Layers

Four well-defined layers ensure separation of concerns, independent scalability, and clean interfaces.

Layer 1 • User-facing interfaces

Presentation Layer

Multi-channel access through responsive web, native mobile, and headless REST/GraphQL APIs serving third-party integrations.

Web Dashboard

Responsive SPA built with modern frameworks — works on desktop, tablet, and mobile browsers.

Mobile Apps

Native iOS & Android apps with offline capability, document scanning, and push notifications.

REST & GraphQL APIs

Comprehensive API layer for headless integrations, third-party systems, and custom workflows.

Admin Console

System administration portal for tenant management, configuration, monitoring, and reporting.

Layer 2 • Core business logic

Application Layer

The heart of Sarthi DMS — orchestrating document workflows, OCR processing, AI/ML intelligence, and business rules.

Business Logic Engine

Configurable rules engine handling document routing, approvals, SLA enforcement, and notifications.

Workflow Engine

BPMN 2.0 compliant engine supporting sequential, parallel, and conditional workflow patterns.

OCR Engine

Multi-language OCR with pre/post-processing pipelines, handwriting recognition, and table extraction.

AI/ML Services

Document classification, entity extraction, sentiment analysis, auto-tagging, and duplicate detection.

Layer 3 • Persistent storage & indexing

Data Layer

High-performance data tier with relational databases, object storage, in-memory caching, and full-text search indexes.

Relational Database

PostgreSQL / MySQL clusters for structured metadata, user data, workflow state, and audit logs.

File / Object Storage

Pluggable storage backends — local NFS, AWS S3, Azure Blob, Google Cloud Storage, or MinIO.

Cache Layer

Redis / Memcached for session management, hot-path caching, rate limiting, and real-time pub/sub.

Search Index

Elasticsearch / OpenSearch powering full-text, semantic, and faceted search across millions of documents.

Layer 4 • Deployment & operations

Infrastructure Layer

Production-grade infrastructure services ensuring high availability, observability, security, and rapid content delivery.

Load Balancer

L4/L7 load balancing with health checks, SSL termination, sticky sessions, and geo-routing.

CDN

Global content delivery network for static assets, thumbnails, and frequently accessed documents.

Monitoring & Logging

Prometheus + Grafana metrics, ELK stack logging, distributed tracing with Jaeger, and PagerDuty alerts.

Container Orchestration

Kubernetes-based deployment with Helm charts, auto-scaling, rolling updates, and self-healing pods.

Tech Stack

Technology Stack

Built with proven, enterprise-grade open-source and commercial technologies

Backend

  • Java / Spring Boot
  • Python (AI/ML services)
  • Node.js (real-time)
  • gRPC & REST APIs
  • Apache Kafka (messaging)

Frontend

  • React / Next.js
  • TypeScript
  • Tailwind CSS
  • Progressive Web App (PWA)
  • React Native (Mobile)

Databases

  • PostgreSQL (primary)
  • MongoDB (documents)
  • Redis (cache)
  • Elasticsearch (search)
  • TimescaleDB (time-series)

AI / ML

  • TensorFlow / PyTorch
  • Tesseract OCR (base)
  • Custom LSTM models
  • Hugging Face Transformers
  • ONNX Runtime

DevOps

  • Docker & Kubernetes
  • Helm Charts
  • Terraform / Ansible
  • GitHub Actions / Jenkins
  • ArgoCD (GitOps)

Monitoring

  • Prometheus & Grafana
  • ELK Stack (Logging)
  • Jaeger (Tracing)
  • PagerDuty (Alerts)
  • Sentry (Error tracking)
Benchmarks

Performance Benchmarks

Real-world performance metrics from production deployments handling millions of documents

<50ms
API Response Time
p95 latency for metadata queries
10K+
Concurrent Users
Per cluster node capacity
99.99%
Uptime SLA
Multi-region HA deployment
50M+
Documents Indexed
Sub-second search response

Throughput by Operation

Document Upload 1,200 docs/min
OCR Processing 800 pages/min
Full-Text Search 5,000 queries/sec
Workflow Execution 2,500 tasks/min
API Requests 15,000 req/sec
Document Download 3,000 docs/min

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