AI Landing Zone on AWS
Secure. Observable. Cost-governed. From day one.
Generative AI
We build the platform.
You build the product.
Agentic AI represents the next frontier in computing - where intelligent agents reason, plan, and act autonomously to complete complex tasks with limited human involvement. Several Clouds helps businesses build AI Landing Zone.
You build the AI features your users need. We build the platform underneath — so every model call is hardened before it hits production.
- Delighted Customers
Deploy autonomous agents that don't just answer questions but resolve issues, manage returns, and schedule services by interacting directly with your APIs. - Empowered Employees
Boost productivity by giving every team a proactive assistant that anticipates needs, summarizes multi-day project shifts, and executes routine administrative workflows. - Accelerated Innovation
Shift from content creation to autonomous ideation. Use agents to run simulations, conduct market research, and generate high-quality technical documentation in hours. - Enhanced Development
Work alongside AI Agents that refactor entire codebases, perform security audits, and manage infrastructure migrations automatically. - Secure and Compliant AI Augmentation
Deploy agentic workflows using your internal data while meeting strict security and compliance standards. Maintain full control over agent permissions and "human-in-the-loop" checkpoints.
Compute, Network, Storage for AI Workloads
The base AWS infrastructure that AI workloads run on. This is cloud engineering — but with AI-specific requirements around GPU instances, VPC endpoints for model APIs, and storage architectures for training data and vector stores. Getting this wrong means latency spikes during inference, unnecessary data transfer costs, and security gaps at the network level.
Why it matters: Your AI application inherits the performance ceiling and security posture of this layer. A misconfigured VPC endpoint means model calls traverse the public internet. An undersized instance class means inference latency kills user experience. We handle this so your team never has to debug infrastructure under an AI workload.
Compute & Networking GPU/Inferentia instance selection and capacity planning. VPC endpoints for Bedrock and SageMaker — no public internet path for model calls. Private subnet architecture for inference workloads. Cross-region inference routing for latency optimization.
Storage & Data S3 bucket architecture for training data, embeddings, and model artifacts. Vector database infrastructure — OpenSearch Serverless, Aurora pgvector, or Kendra depending on query patterns and scale requirements. Data lifecycle policies covering training data retention and embedding refresh cycles. Cross-region replication for disaster recovery of knowledge bases.
Identity & Access IAM policies for Bedrock model access scoped to least privilege per model. Service-linked roles for SageMaker and Bedrock agents. Cross-account model access patterns using the central AI account model. KMS key management for model encryption and data encryption at rest.
The Operating Layer for AI Workloads
This is where Several Clouds creates the most differentiated value. It is the platform engineering layer — the guardrails, policies, and shared services that every AI application in the organization uses. Your developers build on top of this layer; they do not build it themselves.
Why it matters: Without a governed platform layer, every team reinvents the same security controls, builds their own cost tracking, and implements ad-hoc logging. You end up with 5 teams, 5 different guardrail configurations, and no consistent way to audit or control AI usage. This layer is the reason enterprises can scale from 1 AI application to 20 without multiplying risk.
Cost Governance Per-team and per-application token budget enforcement via tagging and alerts. Provisioned Throughput vs. On-Demand analysis and right-sizing. Model selection cost optimization — when to use Haiku vs. Sonnet vs. Opus based on task complexity. Reserved capacity planning for predictable inference workloads. Cost anomaly detection for runaway token consumption and prompt injection cost attacks.
Security Baseline Bedrock Guardrails configuration — content filters, denied topics, PII redaction. Prompt injection detection and mitigation patterns. Data classification for RAG sources — defining what can and cannot enter the context window. Model invocation logging to CloudTrail (who called what model, when, from where). Network security enforcement — no direct internet egress from inference endpoints.
Observability Centralized model invocation metrics: latency, token counts, error rates. Custom CloudWatch dashboards for AI workload health. Alerting on guardrail violations, throttling, and model errors. Cost-per-invocation tracking attributed to business units. Prompt and response logging pipeline for audit.
Shared Services Centralized knowledge base management via Bedrock Knowledge Bases. Shared embedding pipeline — ingest, chunk, embed, index — available to all workload accounts. Model access catalog listing approved models, versions, and endpoints. Prompt template registry with version control. A/B model deployment patterns — canary and blue-green strategies for model swaps.
The Bridge Between Platform and Product
This is the collaboration zone. Several Clouds provides reference architectures, CI/CD patterns, and deployment frameworks. Your developers use these to build their specific applications. Think of it as the golden path — opinionated defaults that are secure, observable, and cost-efficient by design, with flexibility where your application needs it.
Why it matters: This layer is where speed-to-production is won or lost. Without reference architectures, every team starts from scratch. Without CI/CD templates with built-in security checks, teams ship unreviewed AI workloads to production. The golden path means your first AI application takes weeks, not months — and every subsequent one is faster.
Several Clouds provides Reference architectures for common patterns: RAG, agents, chat, summarization. IaC modules (Terraform and CDK) for AI workload deployment. CI/CD pipeline templates for model deployment and evaluation. Automated testing frameworks — prompt regression testing and evaluation harnesses. Deployment guardrails — automated security and cost checks that gate production releases.
Your team implements Application-specific agent orchestration using Bedrock Agents or Step Functions. Custom tool and action group definitions for your agents. Application-specific RAG configuration — chunking strategy, retrieval tuning, reranking. Prompt engineering for your specific use cases and domains. Integration with your existing application backends and data sources.
The AI-Native Product
This is fully your domain — the actual AI-powered product or internal tool that your users interact with. Several Clouds does not build this. But the quality of Layers 1 through 3 directly determines whether this layer can move fast, stay secure, and remain cost-efficient.
Why it matters: This is where your competitive advantage lives. The platform underneath should be invisible to your product team — they should be thinking about user experience, domain logic, and business value, not debugging VPC configurations or writing custom logging pipelines. When Layers 1–3 are solid, your product team ships AI features at the speed of product decisions, not infrastructure tickets.
Product & Experience Conversational UX design and interaction patterns. User-facing features — chat interfaces, copilots, search, summarization tools. Business logic — what the AI agent actually does for your users. Domain-specific prompt engineering and fine-tuning decisions. User feedback loops and response quality evaluation.
Data & Domain Knowledge Training and fine-tuning data preparation. Domain-specific knowledge base content curation. Ground truth datasets for evaluation and regression testing. Business rules that constrain AI behavior for your industry. Compliance requirements specific to your regulatory environment.
Agentic AI on AWS
Several Clouds transforms agentic theory into real business value. We specialize in the AWS Agentic Stack to support:
- Multi-Agent Systems: Orchestrated agents tailored to complex company workflows (e.g., a "Researcher Agent" feeding a "Writer Agent").
- Reasoning and Planning: Agents that use Chain-of-Thought reasoning to break down complex goals into executable steps.
- Tool Use (Action Groups): Giving AI the ability to securely call your internal APIs to "do" work, not just "talk" about it.
- Persistent Memory: Knowledge Bases that allow agents to remember user preferences and past interactions across sessions.
- Automated Modernization: Agents that autonomously upgrade legacy applications to modern cloud versions.
- Integrated guardrails and access controls for responsible use
What We Operate
Six pillars of production-grade AI workloads
Security
● Bedrock Guardrails & content filtering
● Data classification for RAG sources
● Model invocation audit trail
Observability
● Token consumption dashboards
● Latency & error rate monitoring
● Guardrail violation alerting
● Cost attribution per business unit
FinOps
● Per-team token budget enforcement
● Model selection cost optimization
● Provisioned vs. on-demand analysis
● Cost anomaly detection
Reliability
● Multi-region inference routing
● A/B model deployment patterns
● Knowledge base DR & refresh cycles
● AI-specific incident runbooks
Operational excellence
● Automate operational management
● Establish operational controls
Sustainability
● Design for environmental efficiency
● Implement dynamic resource optimization
You focus on building the AI features your users need. We make sure every model call is secure, observable, cost-governed, and production-hardened — so your team ships AI faster without building the platform underneath it.
Assess
AI Readiness Review
We evaluate your AWS environment against the AI Landing Zone blueprint — identity, networking, data architecture, security posture, and cost baseline. You get a report with prioritized gaps.
Architect
Platform Design
We design your AI platform layer — Bedrock and Agent Core access patterns, guardrails configuration, observability stack, cost governance model, and shared services architecture. All documented as IaC-ready blueprints.
Implement
Landing Zone Deployment
We deploy the AI Landing Zone — Terraform/CDK modules, security baselines, monitoring dashboards, cost alerting, CI/CD templates. Your developers get a golden path to production from day one.
Operate
Continuous Optimization
Ongoing model right-sizing, cost optimization, security posture management, guardrail tuning, and quarterly Well-Architected AI reviews. We evolve the platform as your AI workloads grow.
Several Clouds Tools of Choice
Several Clouds use AWS services to deliver AI capabilities:
- Amazon AgentCore
The premier environment for architecting and scaling Agentic AI. It enables Multi-Agent Collaboration, allowing specialized agents to reason, plan, and execute complex, multi-step business workflows autonomously. Built with enterprise-grade security, it ensures that your agents act within defined organizational guardrails. - Amazon Bedrock
Fully managed capabilities that empower generative AI applications to execute multi-step tasks using enterprise data sources and APIs. These agents transition your applications from simple text generation to autonomous action, significantly reducing manual operational overhead.
Where We Go From Here
Your developers ship AI features faster — on a platform that's secure, observable, and cost-governed by design.
Several Clouds serves as a strategic partner for organizations seeking to implement responsible, high-impact, and secure Agentic AI solutions engineered specifically for their business and powered by AWS. Each deployment is tailored to unique industry requirements, use cases, and compliance standards, ensuring reliability from the outset.
The company's approach accelerates time-to-value by integrating pre-built AWS capabilities with deep architectural expertise in autonomous systems. Several Clouds emphasizes responsible AI practices, embedding Amazon Bedrock Guardrails for transparency, security, and fairness throughout the agent lifecycle.
Whether an organization is launching an initial pilot or scaling an autonomous workforce enterprise-wide, Several Clouds ensures that Agentic AI adoption is mission-driven, sustainable, and future-ready.
Relevant Success Stories
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