What We Deliver
Eight precision-engineered offerings across migration, modernization, and AI — each powered by agentic AI tools and priced on outcomes.
Move to Cloud
Factory-scale migration engines that eliminate hardware debt and provider lock-in — powered by agentic AI orchestration.
Datacenter Exit Made Simple
"The 'Easy Button' for Total On-Premise Decommissioning"
The Mission
Eradicate hardware debt by fully decommissioning VMware, Hyper-V, KVM, or Bare-Metal environments through a factory-scale migration engine. DEMS handles the complete lifecycle from discovery through decommissioning, ensuring zero residual infrastructure and maximum cloud efficiency.
The Secret Sauce
Agentic AI Orchestration: Autonomously handles discovery, wave planning, dependency mapping, and validation. No more manual spreadsheets or "guess-and-check" migrations. Our AI agents learn your environment and optimize migration paths in real-time.
Efficiency & Speed
60% more efficient than manual migration approaches. Typical datacenter exits complete 3-4x faster with zero "guess-and-check" workflows. Automated discovery maps 10,000+ VMs in days, not months.
Supported Platforms
VMware vSphere (all versions), Hyper-V, KVM, Citrix XenServer, Bare-Metal servers. Multi-hypervisor environments handled seamlessly with unified orchestration.
Cloud Targets
AWS (EC2, Auto Scaling), Azure (VMs, Scale Sets), Google Cloud (Compute Engine), Oracle Cloud. Optimized for each cloud's native services and pricing models.
The Result
A predictable, high-volume exit with zero residual hardware debt. Workloads perform identically in the cloud as they did on-premise. Complete infrastructure decommissioning with certified asset disposal and compliance documentation.
Migration Architecture
Source Environment
- On-Premise Datacenter
- VMware vSphere
- Hyper-V / KVM
- Bare-Metal Servers
- Legacy Storage
Azetworks AI Engine
- Migration Orchestration
- AI Discovery & Mapping
- Wave Planning
- Automated Replication
- Validation & Testing
Target: AWS Cloud
- Cloud-Native Infrastructure
- Amazon EC2 / Auto Scaling
- Amazon EBS / EFS
- AWS VPC / Transit Gateway
- CloudWatch Monitoring
AWS Transform Integration:Built on AWS Application Migration Service (MGN) and AWS Migration Hub, enhanced with Azetworks AI for intelligent wave planning, dependency mapping, and automated validation. Seamless integration with AWS Landing Zone and Control Tower for governance.
Cloud to Cloud Migration
"The Freedom to Move Across the Hyperscale Divide"
The Mission
Seamlessly shift workloads between AWS, Azure, and GCP to optimize costs, consolidate fragmented ecosystems, or align with specific CSP-native services. C2C eliminates provider lock-in and enables true cloud flexibility without the traditional "Migration Tax" of downtime and data loss.
The Tech Stack
Agentic AI-based assessments combined with bit-for-bit cloning of compute, object storage, and managed databases. Real-time replication ensures zero data loss during transitions.
Supported Migrations
AWS ↔ Azure, AWS ↔ GCP, Azure ↔ GCP, and multi-cloud consolidations. Handles EC2, RDS, S3, Azure VMs, Cosmos DB, Cloud Storage, and managed services.
Orchestrated Cut-Overs
Automated switching mechanism that handles DNS, networking, and load balancer updates simultaneously. Total vendor fluidity with coordinated failover and rollback capabilities.
Cost Optimization
AI analyzes pricing across providers and recommends optimal instance types, storage classes, and reserved capacity. Typical savings: 20-40% through intelligent right-sizing.
The Result
Frictionless Mobility: Near-zero downtime and zero data loss during cross-provider transitions. Eliminate provider lock-in, reduce costs, and maintain full operational continuity throughout the migration.
Migration Architecture
Source Cloud
- Azure / GCP / Oracle
- Virtual Machines
- Managed Databases
- Object Storage
- Networking & Security
Azetworks AI Engine
- Cross-Cloud Orchestration
- AI Cost Analysis
- Real-Time Replication
- Service Mapping
- Automated Cut-Over
Target: AWS Cloud
- Optimized AWS Services
- EC2 / RDS / Aurora
- S3 / EBS / EFS
- VPC / Route 53
- Cost-Optimized Instances
AWS Transform Integration:Built on AWS DataSync, Database Migration Service (DMS), and Transfer Family for seamless cross-cloud data movement. AI-powered cost optimization identifies the best AWS instance types, storage classes, and reserved capacity options—typically achieving 20-40% cost savings compared to source cloud. Integrated with AWS Migration Hub for centralized tracking.
Modernize & Scale
Retire technical debt, escape legacy lock-in, and build cloud-native foundations — all AI-accelerated.
Legacy Re-platforming & Translation
"Retire the Debt. Release the Value."
The Mission
Escape "dead-end" languages (VB6, COBOL, PowerBuilder, Delphi) and aging Java/.NET versions by translating legacy logic into modern, maintainable frameworks. LRT preserves business logic while eliminating technical debt, enabling your applications to run on modern cloud infrastructure with contemporary development practices.
The Tech Stack
Agentic AI Code Mapping: Reads, maps, and rewrites legacy code, extracting core business logic while eliminating redundant "bloat." Uses AST (Abstract Syntax Tree) analysis, pattern recognition, and semantic understanding to preserve intent while modernizing implementation.
Supported Transformations
VB6 → .NET Core/Java, COBOL → Java/Python, PowerBuilder → Angular/React, Legacy Java → Spring Boot, .NET Framework → .NET 8+. Mainframe to cloud-native transformations included.
Efficiency & Speed
70% faster translation than traditional manual rewrite approaches. Typical 100K LOC application translates in weeks, not years. Automated testing ensures functional parity throughout.
Quality Assurance
Automated regression testing, side-by-side validation, and business logic verification. Every transformation includes comprehensive test coverage and documentation.
The Result
Converts your biggest technical liability into your most agile asset. Legacy debt isn't just reduced—it's retired and replaced with a clean, maintainable foundation. Modern CI/CD pipelines, cloud-native deployment, and developer-friendly codebases.
Modernization Architecture
Legacy Applications
- Dead-End Technologies
- VB6 / COBOL / PowerBuilder
- Legacy Java / .NET
- Mainframe Applications
- Monolithic Architecture
Azetworks AI Engine
- Code Transformation
- AI Code Analysis
- Logic Extraction
- Modern Framework Mapping
- Automated Testing
Target: AWS Cloud
- Modern Cloud Applications
- Java/Spring Boot on ECS
- .NET Core on Elastic Beanstalk
- Python/Node.js on Lambda
- CI/CD with CodePipeline
AWS Transform Integration:Transformed applications deploy to AWS Elastic Container Service (ECS), Elastic Kubernetes Service (EKS), or AWS Lambda for serverless. Built on AWS Application Discovery Service for code analysis and AWS Migration Hub for tracking. Integrated with AWS CodeCommit, CodeBuild, and CodePipeline for modern CI/CD workflows. Monitoring via CloudWatch and X-Ray.
Database Liberation
"Break Free From the License Tax."
The Mission
Eliminate punishing Oracle and SQL Server licensing costs by migrating to high-performance, open-source engines built for the cloud era. DBL handles schema conversion, stored procedure translation, data migration, and application refactoring—ensuring zero business logic loss while dramatically reducing TCO.
The Tech Stack
Outcome-Based Conversion: Automated schema conversion, stored procedure translation, and data migration tools. Handles complex PL/SQL, T-SQL, triggers, and business logic with AI-assisted refactoring.
Target Platforms
PostgreSQL, MySQL, MariaDB, Amazon Aurora, Azure Database, Google Cloud SQL. Optimized for cloud-native performance and scalability with managed service integration.
Financial Impact
Dramatic TCO reduction (typically 60-80% savings), total removal of vendor lock-in, and elimination of audit anxiety. Predictable, consumption-based pricing replaces unpredictable licensing costs.
Migration Approach
Zero-downtime migrations using continuous replication, automated validation, and rollback capabilities. Phased cutover ensures business continuity throughout the transition.
The Result
A database layer that scales on your terms and your budget, not your vendor's audit cycle. Modern, cloud-native databases with better performance, lower costs, and complete operational freedom.
Modernization Architecture
Legacy Databases
- Proprietary Platforms
- Oracle Database
- SQL Server Enterprise
- PL/SQL & T-SQL Logic
- High Licensing Costs
Azetworks AI Engine
- Database Transformation
- Schema Conversion
- Stored Procedure Translation
- Continuous Replication
- Automated Validation
Target: AWS Cloud
- Open-Source & Managed
- Amazon Aurora PostgreSQL
- Amazon RDS (MySQL/MariaDB)
- AWS DMS for Migration
- 60-80% Cost Savings
AWS Transform Integration:Leverages AWS Database Migration Service (DMS) for continuous replication with minimal downtime. AWS Schema Conversion Tool (SCT) handles complex PL/SQL and T-SQL transformations. Target databases run on Amazon Aurora for MySQL/PostgreSQL compatibility with 5x performance improvements, or Amazon RDS for fully managed open-source engines. Integrated with AWS Migration Hub for progress tracking.
Cloud-Native Foundation
"From Monolith to Modern Architecture."
The Mission
Transform rigid monolithic applications into flexible, cloud-native architectures using microservices, containers, and serverless patterns. CNE breaks down tightly-coupled systems into independently deployable services that scale on demand, enabling continuous delivery and true business agility on AWS infrastructure.
The Tech Stack
AI-Led Decomposition: Automated domain analysis identifies service boundaries, extracts business capabilities, and generates microservice blueprints. Combines AWS App2Container, containerization strategies, and Infrastructure-as-Code (Terraform/CloudFormation) for synchronized application and infrastructure transformation.
Target Architectures
Microservices on AWS ECS/EKS (Kubernetes), serverless functions on AWS Lambda, event-driven architectures with EventBridge/SNS/SQS. API Gateway for unified service mesh, with observability via CloudWatch and X-Ray.
Deployment Velocity
Shift from quarterly/monthly release cycles to continuous deployment. Automated CI/CD pipelines with AWS CodePipeline enable multiple deployments per day. Independent service scaling reduces blast radius and accelerates innovation.
Operational Excellence
Built-in auto-scaling, self-healing infrastructure, and zero-downtime deployments. Container orchestration handles service discovery, load balancing, and health monitoring automatically. Infrastructure costs align with actual usage patterns.
The Result
An architecture that matches the pace of modern business demands. Teams deploy independently, scale services individually, and respond to market changes in hours instead of months. True cloud-native agility with resilience built in.
Modernization Architecture
Monolithic Applications
- Legacy Architecture
- Tightly-Coupled Monolith
- Manual Deployments
- Single Point of Failure
- Slow Release Cycles
Azetworks AI Engine
- Service Decomposition
- Domain Analysis
- Service Boundary Detection
- Containerization
- IaC Generation
Target: AWS Cloud
- Cloud-Native Services
- ECS/EKS Microservices
- Lambda Serverless Functions
- API Gateway & EventBridge
- Auto-Scaling & CI/CD
AWS Transform Integration:Microservices deploy to Amazon ECS (Elastic Container Service) or EKS (Elastic Kubernetes Service) with Fargate for serverless containers. Built on AWS App2Container for automated containerization. Event-driven workflows use AWS Lambda, EventBridge, SNS, and SQS. API Gateway provides unified service access. Full CI/CD automation via AWS CodePipeline, CodeBuild, and CodeDeploy with blue/green deployments.
Performance & Cross Platform Optimization
"Immediate Gains, No Full Rewrite Required."
The Mission
Deliver rapid performance improvements and cost optimization through targeted platform upgrades—no massive rewrites, no multi-year programs. PXO focuses on high-impact, low-risk transformations like migrating Java workloads to AWS Graviton processors, upgrading runtime versions, and optimizing infrastructure configurations for immediate ROI.
The Tech Stack
Surgical Re-platforming: AI-powered workload profiling identifies optimization opportunities. Automated compatibility testing, performance benchmarking, and IaC-driven deployment to ARM-based AWS Graviton instances. Runtime modernization for Java, .NET, Node.js, and Python applications.
Target Platforms
AWS Graviton2/Graviton3 processors (ARM architecture), latest JDK versions (Java 17/21), .NET 8+, Node.js LTS, Python 3.11+. Optimized for EC2, ECS, EKS, Lambda, and RDS workloads with minimal code changes.
Performance Impact
Up to 40% better price-performance ratio on AWS Graviton vs. x86 instances. 20-30% cost reduction for compute-intensive workloads. Improved energy efficiency and sustainability metrics. Typical migration completes in 2-4 weeks.
Risk Mitigation
Comprehensive compatibility validation, automated regression testing, and phased rollout strategies. Blue/green deployments enable instant rollback. Performance monitoring confirms gains before full cutover.
The Result
Measurable ROI within weeks, not months. Applications run faster, cost less, and leverage modern infrastructure—all without the risk and expense of a complete rewrite. Quick wins that fund larger modernization initiatives.
Modernization Architecture
Legacy Infrastructure
- x86 Architecture
- Legacy Java/JDK 8
- x86 EC2 Instances
- Higher Compute Costs
- Outdated Runtimes
Azetworks AI Engine
- Platform Optimization
- Workload Profiling
- Compatibility Testing
- Performance Benchmarking
- IaC Deployment
Target: AWS Graviton
- ARM-Based Performance
- AWS Graviton3 Processors
- Modern Java 17/21 Runtime
- 40% Cost Reduction
- Enhanced Performance
AWS Transform Integration:Migrate workloads to AWS Graviton2/Graviton3 ARM-based processors for superior price-performance. Built on AWS Migration Hub for tracking and AWS Systems Manager for automated deployment. Compatible with EC2 (M7g, C7g, R7g instances), ECS/EKS containers, Lambda functions, and RDS databases. Automated testing validates compatibility, while CloudWatch metrics confirm performance improvements. Typical 40% cost savings with better throughput.
AI-Powered Workflows
From embedding LLMs into business processes to deploying autonomous AI agents — Azetworks makes AI real, safe, and scalable.
GenAI Transformation
"Turn Enterprise Data into Competitive Advantage."
The Mission
Embed Generative AI into core business processes to transform how organizations work with data, make decisions, and serve customers. GAT converts static enterprise knowledge—documents, databases, legacy systems—into intelligent, queryable assets that power productivity, accelerate decision-making, and create differentiated customer experiences.
The Tech Stack
Azet GenAI Accelerators: Enterprise-grade LLM integration with Retrieval-Augmented Generation (RAG) architectures. Secure data ingestion pipelines, vector databases for semantic search, prompt orchestration frameworks, and guardrails for responsible AI. Built on AWS Bedrock, SageMaker, and OpenSearch.
Use Cases
Intelligent document processing, automated report generation, conversational analytics, knowledge base Q&A, code generation, customer service augmentation, contract analysis, and compliance automation. Domain-specific fine-tuning available.
Business Impact
50-70% reduction in manual knowledge work. Instant access to organizational intelligence across siloed systems. Automated insights from unstructured data. Faster decision cycles with AI-powered recommendations. Enhanced customer experiences through personalized, context-aware interactions.
Security & Governance
Enterprise-grade data privacy with private model deployments. Role-based access control, audit logging, and compliance frameworks (GDPR, HIPAA, SOC2). Content filtering, bias detection, and responsible AI monitoring built in.
The Result
Intelligent applications that don't just store data—they understand it, reason about it, and act on it. Organizations gain a 360-degree uplift in operational intelligence while reducing costs and accelerating time-to-insight from weeks to seconds.
AI Architecture
Enterprise Data
- Static Knowledge
- Documents & Reports
- Databases & Data Lakes
- Legacy Systems
- Unstructured Content
Azetworks AI Engine
- GenAI Processing
- Data Ingestion & Indexing
- RAG Architecture
- Prompt Orchestration
- Guardrails & Governance
Target: AWS AI Stack
- Intelligent Applications
- Amazon Bedrock (LLMs)
- SageMaker (Fine-Tuning)
- OpenSearch (Vector DB)
- Lambda (Orchestration)
AWS Integration:Built on Amazon Bedrock for access to foundation models (Claude, Llama, Titan) with enterprise security. Amazon SageMaker enables custom model fine-tuning. OpenSearch Service provides vector database capabilities for semantic search. AWS Lambda orchestrates workflows, while API Gateway exposes intelligent APIs. Full audit trails via CloudTrail and compliance monitoring.
Agentic AI Transformation
"From Chatbots to Autonomous Digital Workers."
The Mission
Move beyond simple chatbots and Q&A systems to deploy autonomous AI agents that plan, reason, and execute complex multi-step workflows with minimal human intervention. AAT creates specialized digital workers that handle end-to-end business processes—from customer service escalations to data analysis pipelines—with the intelligence to adapt, learn, and improve over time.
The Tech Stack
Multi-Agent Frameworks: Tool-using LLMs with function calling, memory systems, and reasoning capabilities. Azet's Agent Governance Layer provides safety guardrails, monitoring, approval workflows, and continuous learning loops. Built on LangChain, AutoGen, and AWS Bedrock Agents.
Agent Capabilities
Autonomous task planning, multi-step workflow execution, tool integration (APIs, databases, systems), decision-making with context awareness, error handling and recovery, human-in-the-loop escalation, and continuous learning from feedback.
Use Cases
Intelligent customer service agents, automated data analysis and reporting, IT operations automation (AIOps), procurement and approval workflows, compliance monitoring, code review and testing, research and synthesis tasks, and cross-system orchestration.
Governance & Safety
Multi-layer approval workflows for high-stakes actions. Real-time monitoring and audit trails. Configurable autonomy levels (fully autonomous to human-supervised). Rollback capabilities and safety constraints. Performance analytics and continuous improvement feedback loops.
The Result
True AI-driven automation at enterprise scale. Digital workers that don't just answer questions—they complete entire workflows, coordinate across systems, and handle exceptions intelligently. Organizations accelerate operations by delegating complex, multi-step processes to specialized AI agents that work 24/7.
AI Architecture
Traditional Automation
- Limited Chatbots
- Simple Q&A Systems
- Rule-Based Workflows
- No Reasoning Capability
- Manual Escalations
Azetworks AI Engine
- Agent Orchestration
- Task Planning & Reasoning
- Tool Integration
- Multi-Agent Coordination
- Governance & Safety
Target: AWS AI Stack
- Autonomous Agents
- Bedrock Agents (LLM)
- Step Functions (Orchestration)
- Lambda (Tool Execution)
- DynamoDB (Agent Memory)
AWS Integration:Built on Amazon Bedrock Agents for autonomous reasoning and action. AWS Step Functions orchestrates complex multi-step workflows with error handling. Lambda functions provide tool integration (APIs, databases, systems). DynamoDB stores agent memory and context. EventBridge enables event-driven agent triggers. CloudWatch monitors agent performance and safety metrics with automated alerts.
Future Agentic Capabilities
Our autonomous roadmap extends agentic workflows deep into cloud operations, infrastructure management, and continuous optimization.
Cloud Service Provisioning Agents
Specialized agents automate cloud resource provisioning and optimize services across multiple cloud platforms, enhancing efficiency and scalability.
Migration & Modernization Assessment Agents
Agents analyze cloud readiness, calculate TCO, generate migration recommendations, and assist in migrations between AWS, Azure, GCP, and OCI.
Automated Infrastructure & Code Transformation
Infrastructure agents generate and deploy scripts from natural language queries, while connectors enable code assessment and transformation for modernization.
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Our team will assess your environment and recommend the best path forward.
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