AWS AI, Machine Learning & Generative AI
Put Generative AI to Work
Secure enterprise generative AI solutions built with AWS-native capabilities.
The challenge
Where organisations get stuck
Employees spend significant time searching for information, creating content, answering questions and performing knowledge-intensive tasks.
- Lost productivity
- Slow decision-making
- Repetitive work
- Knowledge bottlenecks
- Inconsistent outputs
How Analytics Hive helps
AWS Generative AI & AI Assistants
What the engagement includes
- AI assistants
- Enterprise chat
- Document intelligence
- Retrieval-augmented generation
- Knowledge bases
- AI workflows
- Governance
- Security
- Integration
AWS services we build on
- Amazon Bedrock
- Knowledge Bases
- Bedrock Agents
- S3
- OpenSearch
- Lambda
Expected outcome
From manual knowledge work to intelligent AI-assisted operations.
What we deliver
Tangible outputs, not slideware
Typical artefacts from a ai & machine learning engagement, tailored to the scope we agree with you.
- AI opportunity assessment and prioritised use-case roadmap
- AI and data readiness review
- Generative AI solution architecture with guardrails
- Retrieval-augmented knowledge assistants
- Predictive model development and evaluation
- MLOps pipelines, monitoring and retraining strategy
- Responsible AI governance framework
Our approach
How the work runs
- 01
Assess
Understand your business objectives, current environment, risks and opportunities.
- 02
Architect
Design the right AWS target architecture and transformation roadmap.
- 03
Build
Implement the cloud, data, AI and security capabilities required.
- 04
Secure
Embed security, governance, resilience and compliance into the environment.
Business outcomes
What the business gains
- 01
Faster access to information
- 02
More consistent outputs
- 03
Reduced repetitive effort
AWS context
Built on AWS-native capability
AI & Machine Learning work at Analytics Hive is delivered on AWS and aligned to the AWS Well-Architected Framework.
- Amazon Bedrock and foundation model selection
- Amazon SageMaker AI for training and deployment
- Amazon Kendra and Amazon OpenSearch for retrieval
- AWS Step Functions and Lambda for AI workflows
- Amazon Textract, Comprehend and Transcribe
- SageMaker Pipelines, Model Registry and Model Monitor
- Amazon S3 data lakes as the AI data foundation
Built around enterprise priorities
Security-first
Protect workloads, identities and data with controls designed in from the start.
Scalable
Architect AWS environments that grow with demand instead of constraining it.
Resilient
Design for availability, continuity and tested recovery, not best-effort uptime.
Cost-conscious
Optimise infrastructure spend without compromising performance or resilience.
Next step
Put AI to Work
Related services
More ai & machine learning services
Discover how cloud, AI, and data can revolutionize your business
Tell us about your goals and we will come back to you with a practical next step, whether that is a discovery workshop, an architecture review, or a detailed proposal.
ROI-driven solutions