What should be built first?
Shortlist use cases by business value, data availability, risk, build effort, and support requirements.
AWS Partner AI Services
Build useful AI services on AWS.
We help teams design, build, and operate AWS-based AI solutions using Amazon Bedrock, Amazon SageMaker, Amazon Q Developer, and Amazon Textract.
For organisations that need practical AI delivery with clear data, security, cost, and operating controls.
Amazon Bedrock
Amazon SageMaker
Amazon Q Developer
Amazon Textract
For the CEO
Choose AI work that can be tied to a real business process.
For the CIO / CTO
Put the AWS architecture, integrations, monitoring, and ownership in place.
For the CAIO
Move beyond isolated prototypes into managed AI workflows.
For the CFO / Risk Team
Understand cost, access, audit, and operational risk before scaling.
Before You Build
Four decisions that shape the AWS design.
Most AI projects slow down when these decisions are left open. We resolve them early so the build has a clear route.
How will data be accessed?
Define permissions, network paths, encryption, logging, and audit requirements before data is connected.
Which AWS services are needed?
Map the use case to Bedrock, SageMaker, Q Developer, Textract, and the supporting AWS services.
Who will run it?
Set budgets, alerts, support ownership, model lifecycle processes, and handover requirements.
Service Areas
AWS AI services we support.
Each service area is scoped around a practical build pattern and the controls needed to run it.
Amazon Bedrock applications
Design and build generative AI applications using foundation models available through Amazon Bedrock.
- Assistant and knowledge-search patterns.
- Retrieval-augmented generation for approved data sources.
- Model, prompt, security, and deployment review.
- Guardrails, logging, and cost controls.
Amazon SageMaker platform work
Set up SageMaker for teams building, training, deploying, and governing machine learning and foundation model workflows.
- Model development and deployment workflows.
- Data access across Amazon S3, Redshift, and other sources.
- Catalog, governance, and collaboration setup.
- MLOps, monitoring, and lifecycle controls.
Amazon Q Developer enablement
Help engineering and cloud teams use Amazon Q Developer safely within their development and AWS operations workflows.
- Code assistance, documentation, testing, and review patterns.
- AWS console, architecture, and operational guidance.
- Application upgrade and modernisation support.
- Team usage guidelines and access controls.
Amazon Textract document processing
Use Amazon Textract to extract text, handwriting, tables, forms, and layout data from business documents.
- PDF, image, table, and form extraction.
- Invoice, onboarding, lending, and claims workflows.
- Validation, routing, and exception handling.
- Document pipeline monitoring and scale controls.
Bedrock and SageMaker
How the two services fit together.
Bedrock is used for generative AI application patterns. SageMaker is used for the data, ML, and governance work around them.
Amazon Bedrock
- Foundation modelsUse selected models available through Bedrock.
- ApplicationsBuild assistants, summarisation, content, and reasoning use cases.
- RAG and agentsConnect approved knowledge sources, tools, and workflows.
- ControlsApply guardrails, evaluation, logging, and monitoring.
Amazon SageMaker
- Data and ML workspaceGive teams a controlled place to work with data and models.
- Model developmentBuild, train, deploy, and manage ML workflows.
- Lakehouse and catalogConnect S3, Redshift, third-party data, and catalogue controls.
- MLOpsSupport monitoring, lifecycle management, drift checks, and release processes.
In simple terms: Bedrock handles the generative AI layer. SageMaker supports the data and ML layer. AWS security, monitoring, and cost tools keep the service manageable.
Delivery
How the work is delivered.
A straightforward delivery path from assessment through design, build, controls, and handover.
Assess
Confirm use cases, data sources, constraints, AWS account posture, integrations, and stakeholders.
Architect
Define the AWS services, data architecture, identity model, network pattern, security controls, and cost tracking.
Build
Implement the agreed Bedrock, SageMaker, Q Developer, or Textract pattern with repeatable infrastructure and testing.
Handover
Hand over monitoring, cost controls, model lifecycle processes, documentation, and team enablement.
Contact
Request an AWS AI assessment.
A focused review of your use cases, data sources, AWS service fit, risks, and next build steps.
- Top 3 suitable workloads
- Recommended AWS AI service map
- Data, identity, and governance gaps
- Cost and support considerations
- Practical implementation path