Faster time to value
Proven architectures and practices turn months of trial and error into weeks.
1. Envision and discover
Workshops to find high-value AI use cases, assess data readiness and define success criteria tied to business objectives.
Generative AI Managed Services on AWS, from Model Choice to Daily Operation
For organizations that want generative AI working reliably in production: we choose foundation models on Amazon Bedrock, build RAG and AI applications, set guardrails, and keep tracking quality, latency, token use and cost after launch.
- Audited by AWS for the AWS AI Services Competency
- iSEEit: sales reports and deal summaries automated
- Aavas Financiers: loan application summaries inside the DMS
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AWS Partner, Advanced Tier Services
Audited by AWS on customer work we delivered.
- 200+AWS customer launches
- 9AWS service delivery validations
- 2AWS competencies
Generative AI managed services on AWS are the selection, build and ongoing operation of generative AI applications on Amazon Web Services, using services such as Amazon Bedrock, Amazon Q and Amazon SageMaker. HabileLabs, an AWS Advanced Tier Services Partner with the AWS AI Services Competency, selects and tunes foundation models, builds retrieval-augmented generation (RAG) and AI applications, sets up guardrails and governance, and runs them in production with continuous monitoring of performance, latency, token consumption and cost.
The problems we solve for generative AI teams
Once a generative AI application leaves the pilot stage, three problems usually follow:
- Model performance and cost: balancing accuracy, latency and operating cost while keeping token usage and inference efficient.
- Governance and compliance: setting responsible AI practices, monitoring model outputs and staying aligned with regulatory requirements and ethical standards.
- Scale and integration: scaling AI workloads and integrating generative AI across enterprise systems and multi-account environments.
Our managed service handles all three, from the first model choice to the daily running of the application.
What changes with managed generative AI
What our generative AI clients gain, and how each is delivered.
Security and compliance built in
Enterprise security controls, data privacy protection and governance frameworks keep AI usage responsible.
Lower AI operating costs
Model selection, efficient prompt design and continuous optimization can reduce AI operating costs by up to 40%.
More accurate answers
RAG, prompt engineering and fine-tuning make outputs relevant to your context and more accurate.
Architecture that scales
Cloud-native designs that support enterprise-scale workloads with high availability and performance.
Lower implementation risk
Expert guidance, proven methods and thorough testing frameworks reduce the risk of each rollout.
What our generative AI managed services include
End-to-end support for adopting and operating generative AI on AWS.
Custom AI application development
Production-ready applications for content generation, document processing, conversational AI, code generation and intelligent automation.
RAG and knowledge base management
Retrieval-augmented generation with Amazon Bedrock Knowledge Bases, vector databases and your enterprise data sources, for answers grounded in context.
Prompt engineering and fine-tuning
Optimized prompts and fine-tuning that improve model performance, reduce hallucinations and align outputs with business requirements.
AI governance and security
Guardrails, content filtering, audit logging and access controls for responsible AI usage and regulatory compliance.
Monitoring, optimization and MLOps
Tracking of model performance, latency, token consumption and cost with optimization recommendations, plus CI/CD, versioned prompts and models, and automated tests.
How a generative AI engagement runs
Four stages, from the first workshop to an application that keeps improving.
2. Strategize and design
AI architecture with the right models, data pipelines, security controls and integration patterns for your organization.
3. Build and deploy
Proofs of concept, pilots and production deployments, using proven practices for prompt engineering, RAG and model optimization.
4. Manage and evolve
Ongoing monitoring, cost optimization, governance, model updates and improvements based on usage patterns and feedback.
Client results on AWS
Generative AI on AWS we have delivered
Generative AI managed services: common questions
They cover choosing, building and running generative AI applications on AWS for you: foundation model selection, application development, RAG, guardrails, and day-to-day monitoring of quality, latency, token use and cost.
Mainly Amazon Bedrock for foundation models and Amazon Bedrock Knowledge Bases for RAG, along with Amazon Q, Amazon SageMaker, vector databases and your enterprise data sources.
We evaluate the models available on Amazon Bedrock against your use case requirements, performance benchmarks and cost, then recommend the one that fits and keep reviewing it as models are updated.
We ground answers in your own data with RAG, optimize prompts, fine-tune where it helps, and add guardrails and content filtering. Model outputs are monitored after launch.
We keep managing it: performance monitoring, cost optimization, governance enforcement, model updates and improvements based on usage patterns and feedback, with CI/CD and automated testing for each change.
We bring accelerators and domain experience for healthcare, financial services, retail, manufacturing and technology. Our engagements include knowledge transfer, so your team builds internal AI capability alongside the managed service.