AWS AI/ML Development Services Built for Enterprise Scale on AWS

We work across the full AWS AI and ML service catalogue. For foundation models and generative AI, we use Amazon Bedrock with Claude, Llama 3, Mistral, Cohere, and Amazon Titan. For custom model development, we use Amazon SageMaker for training, evaluation, and deployment. For AI application services, we work with Amazon Rekognition for computer vision, Amazon Textract for document extraction, Amazon Comprehend for NLP, Amazon Kendra for intelligent search, Amazon Polly for text-to-speech, and Amazon Translate. We select the right combination of services for your specific use case rather than defaulting to a single product.

Amazon Bedrock is a fully managed AWS service that provides API access to foundation models from multiple providers — Anthropic (Claude), Meta (Llama 3), Mistral, Cohere, and Amazon (Titan) — all within your AWS account and VPC. The key difference from the OpenAI API is infrastructure control: with Bedrock, your data stays within your AWS environment, never traversing external networks, which is critical for organisations with data residency requirements. Bedrock also includes native features for knowledge bases, guardrails, fine-tuning, and model evaluation that you would need to build separately when using the OpenAI API directly.

Amazon SageMaker is a fully managed platform for the complete ML lifecycle: data labelling, feature engineering, model training, hyperparameter optimisation, evaluation, deployment to inference endpoints, and ongoing monitoring. You need SageMaker when your use case requires a custom model trained on your own data — demand forecasting, predictive maintenance, churn prediction, or fraud detection models that need to learn from your specific business patterns. For generative AI tasks where a foundation model will work well, Bedrock is typically faster to production. For classification, regression, or time-series tasks requiring high domain-specificity, SageMaker is the right tool.

The decision comes down to two questions: does the task require a custom-trained model, and is the primary output generated text or structured prediction? Bedrock is the right choice for generative AI, summarisation, Q&A, document analysis, and conversational applications where a foundation model provides sufficient quality. SageMaker is the right choice for training custom models on tabular data, fine-tuning open-source models on your domain corpus, or running inference at a scale where the token pricing of foundation model APIs becomes cost-prohibitive. Many production AWS AI architectures use both: Bedrock for generative tasks and SageMaker for custom predictive models, connected through Lambda and Step Functions.

Yes. We commonly integrate AWS AI services with on-premises ERPs, CRMs, databases, and file systems using AWS Direct Connect or Site-to-Site VPN for private connectivity, along with API Gateway and Lambda to bridge on-premises REST or SOAP services with AWS AI endpoints. For data ingestion, we use AWS Glue, DMS, or Kinesis depending on whether the pipeline is batch or real-time. Security controls — IAM roles, KMS encryption, PrivateLink — are configured to ensure data never traverses the public internet between your on-premises environment and AWS AI services.

Cost management is built into every AWS AI engagement from the start. We configure AWS Budgets with alert thresholds, use SageMaker Savings Plans and Reserved Capacity for predictable workloads, implement auto-scaling for inference endpoints to eliminate idle compute, route simpler tasks to smaller Bedrock models to reduce token costs, and enable S3 Intelligent-Tiering for training data storage. We also implement SageMaker Spot Training for non-critical training jobs at 60–70% cost reduction compared to on-demand instances. Monthly cost reports with per-service attribution are configured before go-live.

MLOps is the practice of applying DevOps principles to the machine learning lifecycle: automating model training, evaluation, versioning, deployment, and monitoring so that models stay accurate and reliable in production without manual intervention. On AWS, we implement MLOps using SageMaker Pipelines for automated training and evaluation workflows, SageMaker Model Registry for versioning and approval gates, SageMaker Model Monitor for data drift and quality drift detection, CloudWatch for latency and error rate alerting, and CodePipeline or GitHub Actions for CI/CD triggers on code or data changes. The result is a system where a new data drop triggers an automated retraining run, evaluation, and deployment if the new model meets your quality thresholds.

A proof-of-concept integration using Amazon Bedrock or a managed AI service typically takes two to four weeks. A production integration with full IAM controls, VPC configuration, cost monitoring, and connection to your existing AWS estate typically takes six to ten weeks. A custom SageMaker model — including data preparation, feature engineering, training, evaluation, and deployment — runs from eight to sixteen weeks depending on data quality and model complexity. MLOps automation adds a further two to four weeks. We scope every engagement before quoting and the scoping call is free.

No. We can work with a brand-new AWS account and provision the necessary infrastructure from scratch, following AWS Well-Architected principles and your security requirements from the first day. If you have an existing AWS account, we can audit your current setup, identify what is reusable, and build incrementally rather than replacing what works. We handle account structure, Landing Zone setup, IAM, VPC design, and all the foundational configuration that precedes the AI/ML build.

The AWS Well-Architected Framework is a set of design principles and best practices across five pillars: Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Optimisation. For AI/ML workloads, this translates to specific requirements: encrypted data at rest and in transit (Security), auto-scaling inference endpoints (Reliability), right-sized compute for each training job (Performance Efficiency), Spot Training and reserved endpoints (Cost Optimisation), and automated model monitoring and retraining triggers (Operational Excellence). We review every AWS AI/ML deployment against the Well-Architected Framework before go-live and provide a remediation plan for any identified gaps.

We have delivered AWS AI and ML projects for clients in financial services, healthcare, insurance, manufacturing, logistics, retail, and professional services. AWS's compliance certifications — HIPAA, PCI-DSS, SOC 2, ISO 27001, and FedRAMP — make it the preferred cloud for regulated industries. Our architecture patterns are designed to meet sector-specific data handling and audit requirements from the outset. We discuss sector-specific compliance and architecture requirements on the free scoping call.