Kayra Export: Marketplace & E-Commerce Platform (CTO)

Quick Summary (TL;DR)

• Role: CTO (product strategy, architecture, team structure)
• Area: Export-oriented B2B/B2C marketplace and multi-channel e-commerce
• Architecture: .NET 8 Microservices + CQRS + event-driven, React Micro-frontends
• Scale: Multi-channel product, price, stock and order operations
• Basic Integrations: Trendyol, Hepsiburada, Etsy, Faire + payment/logistics integrations
• Highlights: Operational efficiency, omni-channel synchronization, AI-powered automation

Featured Results

• Multi-channel commerce operations moved to a single work surface.
• A common integration approach was established for Trendyol, Hepsiburada, Etsy and Faire.
• The decision, delivery and operation rhythm of the 8-person product and engineering team was shared.
• A secure application layer has been positioned for artificial intelligence-supported catalog and operation automation.

Kayra Export Marketplace Platform

Kayra Export Marketplace is a corporate trading platform that brings export-oriented SMEs to the multi-channel sales ecosystem. The aim was to create a scalable marketplace infrastructure that provides product, price, stock and order management from a single center.

As CTO, I managed product strategy, architectural decisions, team structure and delivery rhythm. I built the core on .NET 8 Microservices, CQRS and AWS, and made the operation modules scalable independently with React Micro-frontends.

Case Study

Problem

Disorganized sales channels, manual catalog updates, and separate operational flows per channel limited scalability. There was a need for a platform that could be managed from a single panel and provide real-time synchronization.

Restrictions

Different marketplace APIs, high data volume, delay-sensitive order/stock-based processes, integration with legacy systems, and limited team capacity were the main constraints.

Approach

The integration layer was established with event-driven architecture; Write/read loads were separated with .NET 8 Microservices and CQRS. With React Micro-frontends, modules can be deployed independently. Infrastructure that scales on AWS; Powered by Elasticsearch, Redis and S3. Bedrock/HuggingFace based content and automation services were positioned in the artificial intelligence layer.

Tradeoffs

CQRS and event-driven approach introduced greater service/operations overhead and eventual consistency cost. Microservices and micro-frontend architecture have increased deployment/observability complexity. Artificial intelligence automation required security, prompt management and quality control processes.

Result

Multi-channel operation consolidated on a single platform; Deployment of new integrations is standardized. With artificial intelligence-supported automation, catalog and operation processes have accelerated and the decision-making time of teams has been shortened.

Architecture Overview

Bounded Contexts

• Catalog management
• Pricing and campaign
• Order and returns management
• Stock and warehouse synchronization
• Marketplace integration layer
• Artificial intelligence content/automation services

Data Stream

Data arriving via channel connectors are normalized, written to the event bus, and distributed to the operation screens via read models. The risk of double transaction is reduced with outbox and idempotency controls.

Messaging and Integration

Used event-driven flows, cross-service gRPC/REST integrations, and SLA-based state machines with MassTransit + RabbitMQ.

Distribution and Operation

Located on AWS (EC2, ALB, RDS, Elasticache, S3, Elasticsearch). Automation was achieved with GitLab CI/CD + Terraform. Established observability with Prometheus/Grafana and ELK.

Tradeoffs

• CQRS decoupled read/write payloads but came at the cost of read model management and eventual consistency.
• Event-driven integration provided scale and flexibility but increased idempotency, retrying, and monitoring complexity.
• Microservices and micro-frontend offered team independence but required deployment and operational coordination.

Impact / Results

• Multi-channel product, price, stock and order operations were combined on a single work surface.
• Deployment of new integrations is tied to a common connector and event flow standard.
• Repetitive tasks in the catalog and operation processes were separated for automation.
• Decision, error and operation signals became traceable in the observability layer.

Project Imprint

• Company: Kayra Export Digital Trade Inc.
• Role: CTO / Technology strategy and architectural leadership
• Team: Product and engineering team of 8 people
• Architecture: .NET 8 CQRS + Microservices, React Micro-frontends
• Cloud: AWS EC2, ALB, RDS, Elasticache, S3, Elasticsearch
• Status: Active and scaling platform
• Model: Omni-channel marketplace + AI-powered automation

Related Projects / Next Case Study

• ABC Logistics: Real-Time GIS Tracking Platform- micro-frontend and real-time operation tracking
• Lindow Labs – Dunelm AR “See in the Room” Experience- real-time visualization and integration architecture
• Mulcol: VR Firestop Assembly Simulation- industrial simulation and interactive experience

FAQ

What distinguishes this platform from other marketplaces?

Export-oriented multi-channel structure, single-panel operation management and artificial intelligence-supported catalog automation are the main distinguishing factors. Additionally, the integration layer offers a standard synchronization model between marketplaces.

Why was CQRS chosen?

It was necessary to separate writing-intensive processes such as ordering and inventory from the reporting/reading side. CQRS made complex business rules more manageable while maintaining performance.

How is Bedrock/HuggingFace positioned?

Bedrock was used for the managed model infrastructure and security/guardrail layer; HuggingFace, on the other hand, was positioned in customized content production and classification scenarios.

How is idempotency achieved in multi-channel integrations?

Double order/double update risks were minimized with channel-based idempotency keys, outbox pattern and state machine flows.

How was observability established?

End-to-end observability was achieved with Prometheus and Grafana metrics, ELK log aggregation, and alarm/monitoring rules for critical flows.

Why were micro-frontends preferred?

Micro-frontend architecture was chosen so that operation modules can be deployed independently and teams can work in parallel.

How was data consistency managed?

Event-driven approach and eventual consistency accepted; Compensation and retry strategies were defined in critical flows.