CloudifyOps Agentic AI Services with Bedrock & SageMaker

CloudifyOps provides professional services to design and implement Agentic AI workflows on AWS using Amazon Bedrock and Amazon SageMaker. These workflows enable autonomous reasoning, decisioning, and execution for use cases across healthcare, logistics, customer engagement, and DevOps/AIOps. Implementations are deployed in customer-owned AWS environments and include the full production stack: architecture, orchestration, data flows, retrieval systems, validation loops, and execution surfaces.

• Amazon Bedrock for reasoning, structured outputs, NL-to-SQL, and runbook mapping
• Amazon SageMaker for evaluation, embedding preparation, data processing, and fine-tuning workflows
• AWS Lambda for stateless orchestration and workflow steps
• Amazon EKS for long-running agent controllers and AIOps orchestration
• Amazon API Gateway for exposing AI workflow APIs
• Amazon EC2 for streaming gateways used in real-time voice workflows

• Amazon Textract for OCR extraction (e.g., healthcare prescriptions)
• Amazon Transcribe for real-time speech-to-text (e.g., outbound calling)
• Amazon Polly for text-to-speech in conversational workflows
• Titan embeddings + Qdrant vector store for semantic memory and FAQ retrieval
• Amazon DynamoDB for session state, incident records, and structured outputs
• Amazon RDS for PostgreSQL for live queries from agent-generated SQL
• Amazon S3 for artifacts, transcripts, structured outputs, and evidence storage

• AWS Systems Manager (SSM) for remediation and operational change execution
• Amazon EKS for Kubernetes runbook enforcement and auto-healing actions
• ALB / Route 53 for webhook and streaming path routing
• Amazon CloudWatch for logs, metrics, dashboards, and KPI monitoring
• Security controls including IAM least-privilege, VPC isolation, AWS Secrets Manager, TLS encryption, and controlled egress to support compliance-aligned deployments

CloudifyOps follows a structured methodology across:

1. Discovery & assessment: evaluate workflows, data sources, AWS environment constraints, agent behaviors, validation rules, and execution boundaries.
2. Target architecture design: define Bedrock/SageMaker-based workflow architecture, perception components, embedding and retrieval strategy, state management (DynamoDB/RDS/S3), execution surfaces (SSM/EKS/Lambda/APIs), and security/network patterns.
3. Configuration & implementation: build reasoning flows and RAG lookups; implement pipelines for OCR + validation (healthcare), embedding search + NL-to-SQL (logistics), real-time STT → reasoning → TTS (voice), and auto-healing orchestration (AIOps); integrate storage/state systems and observability.
4. Monitoring, optimization & validation: establish KPIs (accuracy, cycle time, Tier-1 deflection, MTTA/MTTR, reliability), enable end-to-end observability in CloudWatch, optimize scaling, validate reasoning/SQL/remediation and rollback logic, harden security, and tune for accuracy/latency/cost.
5. Handover & documentation: deliver architecture diagrams, workflow descriptions, IAM mappings, runbooks, retrieval/model settings documentation, and knowledge-transfer sessions.

This engagement delivers professional services in the customer’s AWS account; CloudifyOps does not provide or operate a standalone SaaS platform.

• Design and implement agentic workflows using Bedrock and SageMaker Configure perception, retrieval, reasoning, validation, and action components
• Integrate Textract, Transcribe, Polly, DynamoDB, RDS, EKS, SSM, ALB, and CloudWatch
• Perform optimization, validation, documentation, and knowledge transfer
• Implement security guardrails (IAM, VPC isolation, encryption, secrets management)

• Provision and own AWS accounts/resources used for implementation
• Operate workflows after handover using provided runbooks and documentation
• Manage internal governance, compliance, and approvals for AI-driven workflows
• Maintain domain data, master records, and business logic inputs used by agents