Manufacturing on AWS
Manufacturers sit on more operational data than ever, and most of it is unread. We turn IoT telemetry into natural-language answers, deploy real-time anomaly detection on SageMaker, and connect product, pricing, and supply teams to the same live signal.
Real engagements, real numbers. Where Armakuni has already shipped in this vertical.
Telemetry unread, alerts that miss the customer, manual competitive intelligence, content production as the gate. We have unblocked all four.
70,000 records per minute streaming into storage with no analytical tooling. Product teams cannot ask questions of their own data without writing SQL.
No real-time anomaly detection. Devices operating outside normal parameters go undetected until customer complaints surface the problem.
Retail pricing tracked through third-party tools that miss buy-box winners, 1P/3P seller classification, and key markets like Mexico. Headcount caps coverage.
Every new market means promotional banners and product images recreated by hand. Design becomes the gate on market entry pace.
Not every offering applies in this vertical. Here are the ones that fit, and the angle we take with each.
Manufacturing GenAI pays for itself when it removes the bottleneck between operational data and the people who need it. Natural language interfaces over IoT data, synthetic consumer research, predictive maintenance, dynamic content for new markets. Bedrock and SageMaker, deployed in your account.
Manufacturing analytics sits across IoT streams, ERP, retailer feeds, and warehouse data. We build the pipelines on Glue, Redshift, and Snowflake-or-replace patterns that turn that into one queryable layer with cross-region consolidation and self-service onboarding.
The IoT and ERP systems that run the floor were not built for AI tooling. We modernize them with the AK Way: test coverage, clean architecture, observability, and DORA measurement. So agentic systems can run safely on top.
Connected operations do not pause for a holiday. We run 24x7 ownership of the IoT pipeline, anomaly detection alerts, and data warehouse, with on-call discipline mapped to your factory and retail rhythm.
A flexible engineering bench that includes IoT firmware, edge ML, data engineering, and Solution Architecture under one retainer. The pod scales as priorities shift from anomaly detection to synthetic research to dynamic content.
Six engagements: shipped, in flight, or scopable inside an AWS-funded discovery.
AWS Q Business with custom vocabulary, role-based access control, and S3 data-lake connectors. Analysts ask fleet questions without SQL.
SageMaker endpoints classifying anomaly types by severity, predictive maintenance schedules from usage patterns, and SNS alerts routed by team.
AI-powered scraping across 160 retailers and 1,150+ SKUs in three countries. Buy-box winners, 1P/3P sellers, enforcement screenshots, six daily refreshes.
Bedrock-powered persona simulation grounded in real consumer data. Hypothesis testing in days, not weeks, at a fraction of vendor research cost.
Bulk promotional banner translation across language, currency, and pricing using Bedrock, Titan, and Stable Diffusion. Geo-bottleneck removed.
Bedrock AgentCore-orchestrated agent extracting recipes from social video, customizing for dietary needs, and optimizing for the appliance.
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.