Retail and e-commerce on AWS
Retail and e-commerce platforms run on three problems at once: ingesting POS and channel data, recommending what the customer will actually buy, and giving non-technical leaders analytics they can read. We build event-driven pipelines on EventBridge and Step Functions, deploy Amazon Personalize against real order history, and connect QuickSight Q so business users ask in plain English.
Real engagements, real numbers. Where Armakuni has already shipped in this vertical.
Manual data uploads, every new channel a new connector, generic upsell, smart products without guidance. We have unblocked all four.
Every customer data update is a manual export from POS and a CSV upload. By the time data arrives, the campaign window has passed.
Adora, Clover, Shopify, and others each built as one-off connectors with no unified event interface. Every new system is a new engineering project.
No item-level recommendations based on actual order history. Upsell messaging unrelated to what the customer bought.
Connected appliances capturing telemetry every 15 seconds. Cook-time predictions or operational guidance that does not adapt mid-session.
Not every offering applies in this vertical. Here are the ones that fit, and the angle we take with each.
Retail GenAI is item-level recommendations, smart-product guidance, and content automation. Amazon Personalize for ranked suggestions, Bedrock AgentCore for multi-tool agents, and AWS Bedrock for prompt-engineered prediction without the cost of training a custom model.
Retail data is fragmented by design. POS, e-commerce, ad platforms, customer service. We build the AWS Landing Zone, governed data lake, and QuickSight layer that turns that into a single ask-anything intelligence layer.
Multi-tenant retail SaaS often runs on legacy stacks. We modernize on ECS and Aurora with the AK Way, add MCP layers for reusable system connectivity, and leave the platform with an event-driven core ready for the next AI feature.
Retail contact centres absorb seasonal spikes that double headcount cost for a temporary need. Connect with AI self-service for refund eligibility, gift card balances, showtime, and order status. CRM, ticketing, and loyalty integrated through Lambda.
Black Friday, Cyber Monday, holiday spikes. 24x7 ownership of the campaign automation, recommendation engine, and reporting layer with on-call discipline mapped to your retail calendar.
Six engagements: shipped, in flight, or scopable inside an AWS-funded discovery.
API Gateway, Lambda, S3, Step Functions, and EventBridge replacing manual CSV uploads. Live event capture from Adora, Clover, Shopify with standardized schema.
Reusable JSON-event endpoint for orders, subscriptions, and cancellations across Shopify, Salesforce, and others. Adding a new system is configuration, not engineering.
Trained on real order history with user, item, and interaction datasets. Real-time inference endpoint queryable from your campaign engine. Per-customer ranked suggestions.
Bedrock-powered prediction from incremental temperature telemetry. Replaces expensive custom ML with prompt-engineered reasoning that tightens with each data point.
Bedrock AgentCore agent with three coordinated tools: video processing, recipe extraction, recipe customization. Hours of editorial work compressed to minutes.
Natural language analytics over campaign performance, lead quality, and carrier effectiveness. CEOs and marketing leads get answers without filing engineering tickets.
Named, public references in this vertical. Open the case study for engagement scope, AWS funding, and outcome.
Most engagements open with a fixed-fee, AWS-funded discovery. These three workshops are where it usually starts.
Procurement-grade controls available on request. We run engagements under these regimes routinely.
Your first call is with a solution architect, not a sales exec. The first engagement is usually an AWS-funded assessment.