AWS Gen AI
Build production-ready generative AI applications on AWS. We help businesses harness Amazon Bedrock, SageMaker, and the broader AWS AI/ML stack to deliver real business value.
How We Help
Generative AI on AWS — from proof of concept to production
Generative AI is reshaping how businesses operate. But moving from promising prototypes to reliable, scalable production systems demands deep cloud engineering expertise. MakeCloud bridges that gap — we design, build, and run Gen AI solutions on AWS with the same rigour, security, and reliability we apply to every cloud project.
What we deliver
Amazon Bedrock integration
Tap into leading foundation models — including Claude, Llama, and Titan — via Amazon Bedrock. We help you:
- Select the right models for your specific use cases
- Configure guardrails and safety controls
- Design secure, multi-tenant architectures
- Build scalable applications on top of Bedrock’s serverless APIs
Retrieval-Augmented Generation (RAG)
Ground your AI in your own data so answers are accurate, contextual, and trustworthy. We design and implement RAG pipelines using:
- Amazon Kendra for enterprise search
- Amazon OpenSearch Service for vector and keyword search
- pgvector on Amazon RDS for PostgreSQL-based vector search
We handle ingestion, chunking, embeddings, indexing, and query orchestration so your Gen AI apps reliably reflect your business knowledge.
Custom model training & fine-tuning
When off-the-shelf models aren’t enough, we use Amazon SageMaker to fine-tune models on your proprietary data. Our team manages the full ML lifecycle:
- Data preparation and labeling
- Training and hyperparameter tuning
- Evaluation and testing
- Secure, scalable deployment and MLOps
AI-powered workflows
We embed Gen AI into real business processes, not just chatbots. Using AWS Step Functions and AWS Lambda, we build orchestration pipelines that:
- Automate document processing and summarisation
- Generate and review content
- Classify and route tickets or requests
- Integrate with your existing SaaS and internal systems
Security & governance
Every deployment follows AWS security best practices from day one:
- Data remains in your AWS account
- IAM policies follow least-privilege principles
- Network boundaries and encryption are enforced
- Logging, monitoring, and cost controls are built in
We also help you establish governance around model usage, data retention, and auditability.
Knowledge transfer
We ensure your team can confidently operate and extend what we build. Every engagement includes:
- Detailed documentation and runbooks
- Architecture and data flow diagrams
- Hands-on training and pairing sessions
- Handover workshops for operations and development teams
With MakeCloud, you get more than a Gen AI proof of concept — you get a production-ready platform on AWS that your team can own and evolve.
Why MakeCloud
Our Approach
AWS Certified Engineers
Our team holds multiple AWS certifications across architecture, security, DevOps, and more.
Proven Methodology
A structured delivery approach refined over hundreds of AWS projects, from startups to enterprise.
UK-Based Team
Work directly with our engineers — no offshore hand-offs, no language barriers, no timezone headaches.
Transparent Pricing
Clear, predictable costs with no hidden fees. You always know what you're paying for.
Security First
Every engagement is built on AWS security best practices, compliance frameworks, and zero-trust principles.
Rapid Delivery
We move fast without cutting corners. Most engagements see measurable results within weeks, not months.
What our clients say
“Quality AWS DevOps Engineers can be hard to find but quality is exactly what we found with MakeCloud. Their friendly and pragmatic approach made them a pleasure to work with, and I’d recommend them to anyone.”
“MakeCloud took AWS infrastructure and compliance off our plate, so our team of technical experts could focus on delivering great products for our clients.”
AWS Advanced Tier Services Partner
Ready to get started?
Book a free, no-obligation call with one of our AWS-certified engineers. We'll listen to your challenges, share honest advice, and only recommend next steps if we genuinely think we can help.
AWS Gen AI FAQs
Common questions about our AWS generative AI services.
What AWS services do you use for Gen AI?
We primarily work with Amazon Bedrock for foundation model access, Amazon SageMaker for custom model training and deployment, AWS Lambda and Step Functions for orchestration, and Amazon Kendra or OpenSearch for retrieval-augmented generation (RAG). The right combination depends on your use case.
Do we need our own AI/ML team?
No. We can build and deploy Gen AI solutions end-to-end, then hand over with full documentation and training. For ongoing development, we offer managed support so your team can focus on the business while we handle the infrastructure.
What kind of Gen AI applications can you build?
Common use cases include intelligent document processing, internal knowledge assistants, customer-facing chatbots, content generation workflows, code generation tools, and data extraction pipelines. We help you identify the highest-value use case and build from there.
How do you handle data security and compliance?
All data stays within your AWS account. We implement least-privilege IAM policies, VPC isolation, encryption at rest and in transit, and audit logging. For regulated industries, we ensure your Gen AI workloads meet compliance requirements from the start.
How long does a Gen AI project take?
A proof of concept typically takes 2–4 weeks. Production-grade implementations range from 6–12 weeks depending on complexity, integrations, and compliance requirements. We work iteratively so you see results early.
How do we get started?
Get in touch for a free consultation. We’ll discuss your use case, assess feasibility, and outline a clear path to production — no obligation.