Amazon Bedrock, SageMaker & Clean Rooms
Generative AI, custom ML, and privacy-preserving analytics, built on AWS.
We help enterprises unlock the full potential of AWS's AI and analytics services, from deploying foundation models with Amazon Bedrock to building production ML pipelines on SageMaker and enabling secure cross-party data collaboration with Clean Rooms. Our approach delivers measurable outcomes, not science projects.
AWS AI, engineered for production
AWS gives you the primitives. We turn them into production systems, Bedrock foundation models grounded on your data, SageMaker MLOps you can operate, Clean Rooms configured for real collaboration.
Our AWS engagements are led by senior engineers who ship, operate, and instrument these services against real workloads.
What we deliver
Amazon Bedrock
Generative AI
- Foundation model selection & deployment (Claude, Titan, Llama)
- RAG pipelines with Amazon Bedrock Knowledge Bases
- Agents for Bedrock, autonomous task orchestration
- Guardrails configuration for responsible AI
- Model evaluation & A/B testing frameworks
- Fine-tuning & continued pre-training workflows
Amazon SageMaker
Custom ML
- End-to-end ML pipeline design & automation
- SageMaker Studio notebook environments & experiment tracking
- Model training, tuning & real-time/batch inference
- MLOps with SageMaker Pipelines & Model Registry
- Feature Store implementation for reusable ML features
- Cost-optimized training with Spot instances & multi-model endpoints
AWS Clean Rooms
Privacy-Safe Collaboration
- Privacy-preserving data collaboration design
- Clean room configuration with custom analysis rules
- Cross-organization analytics without raw data sharing
- Audience overlap & attribution modeling
- Differential privacy & cryptographic computing controls
- Integration with AWS analytics stack (Athena, Redshift, Lake Formation)
How we engage
Assess
Evaluate your AWS footprint, identify high-value AI & analytics use cases, and map data readiness.
Architect
Design a secure, scalable AWS architecture spanning Bedrock, SageMaker, and Clean Rooms.
Accelerate
Deliver iterative builds with rapid prototyping, stakeholder demos, and production hardening.
Operationalize
Embed CI/CD, monitoring, cost governance, and team enablement for long-term success.
Technology stack
- Amazon Bedrock
- Amazon SageMaker
- AWS Clean Rooms
- Amazon S3
- AWS Lake Formation
- Amazon Redshift
- Amazon Athena
- AWS Lambda
- Amazon CloudWatch
- AWS IAM
What you'll receive
- AWS AI & data strategy roadmap
- Bedrock generative AI solution architecture
- SageMaker MLOps pipeline implementation
- Clean Rooms collaboration framework
- Security & governance blueprint
- Operational runbooks & team enablement
Selected engagement
Selected engagement
Amazon Bedrock & SageMaker for Healthcare Analytics
The Challenge
A large healthcare organization needed to extract actionable insights from millions of unstructured clinical documents while maintaining strict HIPAA compliance and enabling privacy-safe collaboration with research partners.
Our Solution
We deployed Amazon Bedrock for document understanding and summarization, SageMaker for custom clinical NLP models, and AWS Clean Rooms to enable secure research collaboration without exposing patient data.
The Result
Time-to-insight reduced by 45%, research collaboration cycles shortened from months to weeks, and the organization maintained full regulatory compliance throughout, passing three external audits with zero findings.
Frequently
asked
"Synthis brought the architectural clarity we needed. They connected Bedrock, SageMaker, and our existing data lake into a cohesive platform that our data science team actually wants to use, and our compliance team trusts."
VP of Data & Analytics
Large Healthcare Organization
Ready to accelerate with AWS?
Let's discuss how Bedrock, SageMaker, and Clean Rooms can transform your AI and data strategy.