AI-Powered Amazon Analytics Platform on AWS
AmaliTech built a production-grade multi-tenant SaaS platform on AWS EKS, delivering real-time content monitoring, 60+ Kinesis Firehose streams for advertising data ingestion, AI-powered analytics via Amazon Bedrock, and queue-depth autoscaling from 1 to 100 pods with zero long-lived credentials.
Germany
AI
E2E Product Delivery
A Leading E-Commerce Analytics Company
The Business Challenge
The client faced a critical operational gap where live Amazon detail pages diverged from intended master content without seller knowledge, causing undetected content drift that reduced conversion rates and wasted advertising spend. Sellers managing thousands of ASINs had no scalable mechanism to detect when Amazon-side edits overrode catalogued content, and without a master content system, they could not distinguish authorised edits from unauthorised third-party changes.
Additionally, manual data access through Amazon Seller Central required multi-hour export and transformation cycles, with no AI-assisted interface for natural language queries. The impending Amazon Advertising API V2 deprecation created an existential risk to existing reporting integrations.
E-Commerce Analytics
Our Solution
1.
Multi-Tenant SaaS Platform on EKS
We built a production-grade multi-tenant platform on Amazon EKS (Kubernetes 1.33) across two fully isolated AWS accounts — staging and production — provisioned from the same Pulumi TypeScript codebase with environment-specific variable overrides and GitHub Actions CI/CD using OIDC federation.
2.
Real-Time Data Pipeline Architecture
Implemented 60+ Amazon Kinesis Data Firehose delivery streams with dynamic partitioning and GZIP compression, ingesting advertising and sales data across Sponsored Products, Brands, Display, and DSP into Amazon Redshift and S3 data lake with year/month/day/hour partitioning.
3.
AI-Powered Analytics Assistant
Deployed Amazon Bedrock with Knowledge Bases and AgentCore to power an integrated AI chat assistant enabling natural language querying of advertising and catalogue analytics data without SQL or analyst involvement.
4.
Queue-Depth Autoscaling
Architected RabbitMQ-driven microservices with queue-depth-based pod autoscaling — vc-crawler scales 1→100 pods, advertising worker 1→30, and enricher 1→20 — enabling burst workload handling without manual intervention.
5.
Zero-Trust Security & Observability
Implemented IRSA for all 20+ Kubernetes workloads with scoped IAM roles and zero wildcards, AWS KMS for encryption, and full-stack observability via Prometheus, Grafana, and Loki with cross-account monitoring via VPC Endpoint Services.
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