Generative AI Applications
We build generative ai application builder on aws using Amazon Bedrock, covering text generation, summarisation, document Q&A, code assistants, and content workflows.
AWS AI Services
AWS AI Services
Anet Automation helps organisations build and deploy Generative AI on AWS using managed services, foundation models, and enterprise-grade AWS Infrastructure. From initial architecture to full deployment, our AWS Gen AI Services team delivers AI systems that are secure, cost-efficient, and built to last.
Get Your Free AWS Assessment80 to 95%
AI workloads deployed within AWS environments
40%
lower cost compared to external AI APIs
99.9%
uptime on AWS Infrastructure
Client We serve
Enterprise ITSaaSBFSIHealthcareTelecomManufacturingMediaEdTechRetailLogisticsPublic SectorStartupsEnterprise ITSaaSBFSIHealthcareTelecomManufacturingMediaEdTechRetailLogisticsPublic SectorStartups
AWS AI and Gen AI Capabilities
What We Build Using AWS Gen AI Tools
Our AWS Gen AI Services experts help organisations move from AI interest to working AI systems using AWS-native services and architectures.
Custom ML Models
We train, fine-tune, and deploy custom machine learning models on Amazon SageMaker, tailored to your data and use case.
Conversational AI
Chat and voice interfaces built on AWS services, designed for customer support, internal assistants, and enterprise automation.
AI-Powered Analytics
Embedding AI into your data pipelines to surface insights, detect patterns, and automate reporting in real time.
AI in Enterprise Applications
Integrating AI capabilities directly into your existing enterprise software through AWS AI Solutions and API-driven services.
AWS AI Differentiators
Why Build Your Generative AI on AWS With Anet Automation
AWS-Native Architecture
Every solution follows AWS best practices and Well-Architected principles.
Private and Secure by Default
AI workloads run inside private AWS environments using VPC, IAM, and KMS. Your data does not leave your AWS Infrastructure.
High-Performance Compute
GPU-enabled workloads on Amazon EC2 for inference-heavy and training-intensive AI applications.
Cost-Optimised from the Start
We use AWS pricing models, auto scaling, and right-sizing to keep your AWS Gen AI Platform efficient and within budget.
Built on Proven AWS Gen AI Tools
From Amazon Bedrock and SageMaker to open-source models on EC2, we use the AWS Foundation Model ecosystem to deliver reliable, production-grade outputs.
AI Readiness on AWS
Before We Build, We Assess
Rushing into AI without a clear plan is one of the most common reasons projects stall. Our AWS Generative AI Consulting process starts with a structured readiness assessment so your investment goes in the right direction.
- ✓Use case discovery mapped to suitable AWS Gen AI Services
- ✓Data readiness review across S3, data lakes, and existing pipelines
- ✓AI architecture recommendations using AWS reference patterns
- ✓Cost modelling using AWS pricing tools to set realistic expectations
Frameworks used:
AWS Well-Architected FrameworkAWS Cloud Adoption Framework
86%
Of Enterprise AI Projects Fail Due To Poor AI Readiness.
Get An Audit Of Your Project Maturity, Architecture And Governance Before You Invest In Development
Grab your AI Readiness ReportAWS AI Deployment Models
AWS Gen AI Deployment Options
Our AWS GenAI professionals design and deliver AI systems across five deployment models depending on your use case, data sensitivity, and scale requirements.
Model 1 of 5
Foundation Models via Amazon Bedrock
Access and customise leading AWS Foundation Model options including Claude, Titan, and Llama through Amazon Bedrock for fast, managed generative AI applications.
AWS AI Implementation Methodology
Our AWS Solution Development Process
A structured AWS Solution Development approach that covers every phase from discovery to optimisation.
- 1
Assess
Evaluate AI use cases, data readiness, and fit with AWS Gen AI Services.
- 2
Design
Architect the solution using appropriate AWS AI Solutions and infrastructure patterns.
- 3
Build
Integrate models via Amazon Bedrock, SageMaker, or EC2 based on the deployment model selected.
- 4
Deploy
Release to production using scalable AWS Infrastructure with security controls in place.
- 5
Optimise
Tune performance and cost continuously using AWS-native monitoring and optimisation tools.
AWS AI Architecture Patterns
Proven AWS Gen AI Architecture Patterns We Implement
Our AWS GenAI solution experts apply established architecture patterns to reduce implementation risk and accelerate delivery.
- P1Retrieval Augmented Generation (RAG) using Amazon Bedrock and S3
- P2Real-time inference via SageMaker endpoints
- P3AI microservices using API Gateway and Lambda
- P4Containerised AI workloads on Amazon Elastic Kubernetes Service
- P5Event-driven AI pipelines for automated data processing
The AWS Gen AI Platform Stack We Work With
Amazon Bedrock
AI and ML
Amazon SageMaker
AI and ML
Bring the Power of AWS Gen AI to Your Organization
Hold our structured methodology to build, deploy, and optimize your next-gen AI applications.
Security and Responsible AI
Secure AI on AWS Infrastructure
Security is built into every layer of our AWS Gen AI deployments, not added after the fact.
- Private deployments inside AWS VPC, with no data sent to external AI APIs
- Encryption at rest and in transit using AWS-native services
- IAM-based access control with least-privilege policies
- Audit logging and monitoring via CloudWatch and CloudTrail
- Responsible AI governance covering model behaviour, bias controls, and output review
Cost Optimisation
Keeping Your AWS Gen AI Costs Under Control
AI workloads can be expensive if not architected correctly. Our custom AWS Gen AI services are designed with cost efficiency as a first-class requirement.
- Optimise inference costs by selecting the right EC2 instance types and SageMaker configurations
- Reduce reliance on third-party AI APIs by running private models on AWS Infrastructure
- Auto Scaling to handle variable workloads without over-provisioning
- Cost tracking and allocation using AWS Cost Explorer and tagging strategies
Scalable AI on AWS
AI Systems That Scale With Your Business
Our aws infrastructure services are built to handle growing AI workloads without requiring architectural rework.
S1
Auto Scaling AI workloads across EC2, SageMaker, and EKS
S2
High availability architectures with multi-AZ deployments
S3
Real-time AI processing for latency-sensitive applications
S4
24/7 enterprise AI systems with automated failover and recovery
Industry Use Cases
AWS Gen AI Use Cases Across Industries
Our AWS GenAI Partners Company has delivered AI solutions across multiple sectors. Here are some of the most common AWS Gen AI Services use cases we have built:
HR Technology
AI recruitment platforms that screen candidates, generate interview summaries, and surface talent insights using Generative AI on AWS.
Enterprise Analytics
AI analytics platforms that connect to existing data warehouses, run natural language queries, and generate automated reports via Amazon Bedrock.
Conversational AI
Voice and chat AI systems for customer support, internal helpdesks, and guided workflows, built on scalable AWS Gen AI Platform infrastructure.
AI Copilots and Assistants
Embedded AI assistants within enterprise applications that help users draft content, summarise documents, retrieve information, and complete tasks faster.
- ✓Faster AI adoption with a clear AWS-native architecture from day one
- ✓Lower infrastructure costs through right-sized AWS deployments
- ✓Real-time AI insights embedded into existing business workflows
- ✓Secure, compliant AI systems that meet enterprise governance requirements
Engagement Model
How We Work With You
Whether you are exploring AI for the first time or ready to scale an existing system, our AWS Consulting partner engagement model adapts to where you are.
AWS AI Assessment
A structured review of your current infrastructure, data landscape, and AI readiness. We identify the right AWS Gen AI Services for your use case and outline a practical roadmap.
Proof of Concept
A focused, time-boxed build using AWS services to validate your AI use case before committing to full-scale development.
Full-Scale Deployment
End-to-end AWS Solution Development covering architecture, build, testing, and production deployment with full documentation.
Managed AI Operations
Ongoing support, monitoring, and optimisation of your AWS Gen AI Platform post-launch, handled by our AWS Gen AI Services experts.
Why Anet Automation
Why Choose Anet Automation as Your AWS Gen AI Partner
When you hire AWS developers through Anet Automation, you get a team with hands-on experience across the full AWS AI stack, not generalist consultants learning on your project.
- ✓Certified AWS AI professionals with production deployment experience
- ✓End-to-end capability from AWS Generative AI Consulting to managed operations
- ✓Deep familiarity with Amazon Bedrock, SageMaker, and EC2-based AI architectures
- ✓Security-first approach with all workloads running in private AWS Infrastructure
- ✓Transparent cost management and clear project scoping before work begins
- ✓Proven delivery across enterprise, startup, and mid-market organisations
Ready to Simplify Your Cloud Operations
Let our cloud experts manage the complexity, so your team can focus on growth, not infrastructure.