Gen AI, Agentic AI, and ML Implementation on AWS

Armakuni's Gen AI, Agentic AI, and ML Implementation service operationalises AI on AWS. Where our Gen AI and Agentic AI POC engagement validates feasibility, this service delivers the production system. Built for the reliability, security, and governance enterprises depend on.

We work across the complete AWS AI and ML portfolio, selecting the right services for the use case rather than forcing a single approach. Common engagements include autonomous AI agents and multi-agent systems on Amazon Bedrock AgentCore, intelligent document processing, custom ML models for fraud detection and demand forecasting, enterprise RAG on Bedrock Knowledge Bases, conversational AI, and computer vision for quality control or identity verification.

Discovery and Architecture Design. We review your AWS footprint, validated POC outcomes, production requirements, and compliance needs. We select the right combination of AWS AI and ML services for the use case and design a production architecture covering data pipelines, model serving, monitoring, security, and integration with existing systems.

Data Foundation and Knowledge Engineering. We prepare production data pipelines, configure vector stores and Bedrock Knowledge Bases for RAG, set up SageMaker feature stores where applicable, and establish data governance and quality controls. For document intelligence, we design ingestion pipelines using Amazon Textract and Amazon Comprehend.

Core AI Implementation. Our AI engineers build and deploy using the right AWS services for the workload:

• Amazon Bedrock with the Amazon Nova foundation model family (Nova 2 Lite, Nova 2 Pro, Nova 2 Omni) for Gen AI workloads
• Amazon Bedrock AgentCore for Agentic AI, including multi-agent workflows with Runtime, Memory, Gateway, Identity, and Observability
• Amazon SageMaker for custom model training, batch inference, and MLOps
• Amazon Comprehend and Textract for document intelligence
• Amazon Lex, Polly, and Transcribe for conversational interfaces
• Amazon Rekognition for computer vision
• Amazon Forecast and Personalize for predictive analytics
• Amazon Q Developer for code modernisation and developer productivity

Every component ships with Infrastructure as Code in Terraform, AWS CDK, or CloudFormation. Based on our delivery experience, we recommend the right tool for your context - rather than applying a single approach across every engagement.

Integration, Guardrails, and Production Hardening. We integrate AI components with existing systems and implement Bedrock Guardrails for content filtering and safety controls appropriate to the industry. Includes security review, IAM and data governance, performance and load testing, and cost optimisation.

Observability, Handover, and Enablement. CloudWatch monitoring and alerting across all AI and ML services. MLOps pipelines for continuous training and drift monitoring where applicable. Knowledge transfer sessions, full architecture documentation, and operational runbooks.

Scope, AWS service selection, and timeline vary by use case complexity, data readiness, and existing AWS footprint. This service is the production-grade continuation of Armakuni's Gen AI and Agentic AI POC Implementation and can also be initiated independently for organisations with existing validated AI use cases.

Key Deliverables and Outcomes

• Production Gen AI, Agentic AI, or ML system on AWS with full source code ownership
• Infrastructure as Code in Terraform or CloudFormation or CDK for full reproducibility
• Bedrock applications with application inference profiles, Guardrails, and prompt management configured
• AgentCore-deployed agents with Runtime, Memory, Gateway, and Identity where applicable
• Multi-agent workflows that reason, retrieve data, and take action end-to-end
• MLOps pipeline for continuous training and drift monitoring where applicable
• Document intelligence or NLP pipeline integrated into business workflows
• Computer vision or predictive analytics workloads deployed and tested where applicable
• CloudWatch dashboards and alerting for full system observability
• Architecture documentation, operational runbooks, and knowledge transfer sessions
• Cost optimisation baseline and AWS consumption forecast