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Engineering & Systems

Built for National Scale

Production architecture, cloud deployment configurations, distributed datastores and edge integrations designed to scale safely to hundreds of millions of identity profiles.

01

Platform Engineering

Cloud-native platforms — Kubernetes, microservices and multi-region HA.

Platform Engineering — server infrastructure

Overview

Cloud-native platforms — Kubernetes, microservices, service mesh and multi-region architecture engineered for high availability, disaster recovery and elastic scaling.

Systems Architecture

Cloud Native Architecture Kubernetes / Helm Service Mesh (Istio / Linkerd) API Gateway API Management Microservices Architecture Multi-Tenant Platforms High Availability

Key Capabilities

  • Cloud-Native Architecture Containerised microservices on Kubernetes with Istio service mesh for observability, mTLS and traffic control.
  • Multi-Tenant Platforms Secure tenant isolation for many agencies on shared infrastructure with dedicated resource guarantees.
  • High Availability & DR Multi-region, active-active deployment with automated failover and disaster recovery tested through chaos engineering.
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02

Cloud Computing

Multi-cloud, hybrid cloud and edge computing for identity at any scale.

Cloud Computing — global data network

Overview

Multi-cloud and hybrid-cloud architecture for identity infrastructure — AWS, Azure and GCP with cloud-agnostic abstractions, FinOps controls and edge computing where latency demands it.

Systems Architecture

Cloud Computing Multi-Cloud (AWS / Azure / GCP) Hybrid Cloud Edge Computing Cloud-Native Architecture Serverless / FaaS Cloud Migration Data Residency Controls

Key Capabilities

  • Multi-Cloud Strategy AWS, Azure and GCP deployments with Terraform/Pulumi abstractions that prevent lock-in and enable geographic choice.
  • Hybrid Cloud Secure connectivity between on-premise identity systems and cloud services — critical for sovereign and air-gapped deployments.
  • Edge Computing Identity processing at the edge — reduced latency for remote regions, offline capability and data residency compliance.
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03

Distributed Systems

Event-driven architecture, message streaming and high-throughput data systems.

Distributed Systems — event-driven architecture diagram

Overview

The distributed-systems layer underpinning Mantra platforms — event-driven architecture with Apache Kafka, distributed databases, CQRS/event-sourcing and high-throughput processing patterns.

Systems Architecture

Distributed Systems Event-Driven Architecture Apache Kafka Apache Flink Apache Spark Message Streaming CQRS Event Sourcing

Key Capabilities

  • Event-Driven Architecture Apache Kafka and NATS for real-time identity events, fraud signals and audit streams at millions of events per second.
  • Distributed Databases Polyglot persistence — PostgreSQL, CockroachDB, Redis, Elasticsearch, Neo4j and time-series — chosen per workload with global replication.
  • High-Throughput Processing Stream processing with Flink and Spark for real-time fraud detection and identity analytics.
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04

Full Stack Engineering

Backend, frontend, web, mobile and desktop — complete identity surfaces.

Full Stack Engineering — code on screen

Overview

Full-stack engineering spanning backend services, modern frontend applications, progressive web apps, native and cross-platform mobile, and desktop clients — all grounded in secure-by-design and accessible-by-default principles.

Systems Architecture

Python Go (Golang) Java / Spring Boot .NET / C# Node.js REST APIs GraphQL gRPC

Key Capabilities

  • Backend Engineering Python, Go, Java (Spring Boot), Node.js and .NET services — REST, GraphQL and gRPC APIs built for security and throughput.
  • Frontend Engineering React, Vue.js and Angular SPAs with TypeScript — accessible, internationalised and optimised for government and enterprise users.
  • Mobile Engineering Native iOS (Swift/SwiftUI) and Android (Kotlin/Jetpack) plus React Native and Flutter for biometric capture and citizen apps.
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05

AI Engineering

The MLOps backbone that trains, ships, monitors and governs every model.

AI Engineering — model training pipeline

Overview

The MLOps backbone that trains, ships, monitors and governs every model — with full lineage, drift detection and Responsible-AI controls.

Systems Architecture

Data Engineering Feature Engineering Model Training Model Evaluation MLOps Model Monitoring Model Governance Continuous Learning Pipelines

Key Capabilities

  • Training Pipelines Reproducible, distributed training with automated data versioning.
  • MLOps & CI/CD Automated promotion, rollback and a versioned model registry.
  • Monitoring & Drift Live accuracy, drift and per-cohort fairness tracking.
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06

Edge & Device Engineering

Intelligence on the device — from silicon and firmware to edge AI.

Edge & Device Engineering — biometric device

Overview

From silicon and firmware to edge AI and secure connectivity — engineering that brings capture and matching to the device.

Systems Architecture

Embedded Systems Firmware Engineering Edge AI Device Management IoT Connectivity Secure Device Communication OTA Updates Sensor Integration

Key Capabilities

  • Embedded & Firmware Purpose-built firmware for biometric capture devices.
  • Edge AI Quantised, compressed models run on-device in under 200ms.
  • Device Management OTA updates and fleet management at national scale.
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07

Integration Engineering

Connecting identity across standards, systems and ecosystems.

Integration Engineering — connected systems

Overview

OpenID Connect, OAuth2, SAML, SCIM, LDAP and DPI integration that make Mantra interoperable with any platform — federation across organisations and borders.

Systems Architecture

API Management Identity Federation OpenID Connect OAuth2 SAML SCIM 2.0 LDAP Active Directory

Key Capabilities

  • Identity Federation Federate trust across organisations and borders.
  • Open Standards OpenID Connect, OAuth2, SAML and SCIM out of the box.
  • DPI Integration Native integration with MOSIP, eSignet and Inji.
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08

Reliability Engineering

SRE, observability, resilience and performance for identity infrastructure.

Reliability Engineering — monitoring dashboard

Overview

Site Reliability Engineering for identity — observability, distributed tracing, capacity planning and chaos engineering that keep systems always-on.

Systems Architecture

Site Reliability Engineering (SRE) Observability Distributed Tracing Capacity Planning Resilience Engineering Chaos Engineering Performance Engineering Incident Management

Key Capabilities

  • Observability Metrics, logs and distributed tracing across services.
  • Resilience Engineering Chaos testing and graceful degradation.
  • Capacity Planning Scale ahead of national demand spikes.
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Build for Scale

Talk to our engineering and research teams about applying these capabilities to your country programme, enterprise platform or fintech stack.