Artificial Intelligence on AWS

The AWS assessment covers feasibility, account placement and cost together, because on this platform the cost model is a design input rather than an afterthought.

• An account and workload review covering AWS Organizations structure, IAM boundaries and where the AI workload should sit relative to existing production accounts.
• A source-data inventory across Amazon S3 and connected systems, recording format, volume, refresh frequency and who owns access to each set.
• A model availability check in the regions you intend to use, recorded with the verification date, alongside the shortlist of models suited to your task.
• A volume and token model estimating monthly inference cost at expected and peak usage, together with the Amazon OpenSearch Service sizing your retrieval design implies.
• A build-versus-adopt recommendation stating where Amazon Q Business covers the need and where a custom Bedrock application is justified, with connector coverage checked against your actual systems.
• A responsible-AI review defining guardrail policies, human approval points and the actions an agent must never be permitted to take unattended.

Patterns are assembled from managed services rather than a single product, which gives precision at the cost of more decisions. We make those decisions explicit and record why each was taken.

• Retrieval on Amazon OpenSearch Service with a documented chunking and embedding strategy, metadata filters for team or tenancy scoping, and permission checks applied before results reach the model.
• Amazon Bedrock as the model layer, with Amazon Bedrock Guardrails configured for denied topics, sensitive-information filtering and grounding checks, applied consistently across every application that calls a model.
• Agent behaviour defined with Amazon Bedrock Agents against an explicit tool schema, using Amazon Bedrock AgentCore where sessions need managed runtime, memory and identity handling.
• Orchestration through AWS Step Functions for multi-step document work, AWS Lambda for extraction and enrichment, and Amazon EventBridge triggering on new objects in Amazon S3 instead of polling.
• Amazon API Gateway in front of application endpoints for authentication, throttling and usage plans, keeping direct model access off any public surface.
• Predictive models trained and registered in Amazon SageMaker AI, with Amazon SageMaker Unified Studio used where data engineering, analysis and model work need one workspace over the same governed data.

On AWS almost nothing is enabled by default, which is an advantage if the controls are designed in and a liability if they are left until later. We build them in the first increment.

• AWS Identity and Access Management roles scoped per component, with separate roles for retrieval, inference and each tool an agent may call, and no shared long-lived credentials anywhere in the path.
• Amazon Bedrock Guardrails maintained as a shared policy set, with denied-topic and sensitive-information rules tested in the release pipeline rather than configured by hand per application.
• Retrieved content treated as untrusted input, with prompt-injection cases held in the evaluation suite and tool invocation restricted to an allowlist with argument validation.
• Amazon S3 bucket policies, encryption and access logging reviewed for every source in scope, with a published list of buckets deliberately excluded from indexing.
• Every model call, retrieval and tool invocation logged to Amazon CloudWatch with correlation IDs, so any answer can be reconstructed during an internal review.
• Human approval enforced as a task inside AWS Step Functions before an agent takes any action with financial, contractual or client-facing consequence.

AWS assistants usually appear inside your own application or a managed assistant you switch on for one team, so adoption is planned per cohort and per workflow rather than per tenant.

• A pilot cohort of named users with a weekly review of unhelpful answers and a documented list of the questions the system is not expected to handle.
• Amazon Q Business rolled out to one business area first, with connector coverage confirmed against the systems that team actually uses before licences are assigned more widely.
• Amazon Q Developer introduced to engineering with agreed conventions on review, testing and what may not be accepted into a codebase without inspection.
• Task-level enablement built around the specific workflows in scope, with prompts and worked examples written from your own documents.
• A published statement of intended use, known limitations and escalation path, owned by the business sponsor rather than by the platform team.
• Measurement against the assessment baseline, reported to the sponsor with the original assumptions restated so the comparison remains defensible.

A generative workload on AWS has many moving parts and many small meters. Continuation keeps quality, security posture and cost under the same review each month.

• The evaluation suite re-run on a schedule and on every model or prompt change, with results trended and regressions raised before users report them.
• Amazon CloudWatch dashboards and alarms on invocation errors, throttling, latency percentiles, consumption per feature and retrieval quality signals.
• Guardrail policy review as new content types and use cases appear, including re-testing denied topics and sensitive-information filters against fresh examples.
• Bedrock model lifecycle tracking, with a migration path tested and ready before a model version is withdrawn.
• Amazon OpenSearch Service cluster health, index freshness and ingestion failure review, with sizing revisited against actual query volume rather than the original estimate.
• Monthly cost attribution by feature and account, comparing real inference, retrieval and orchestration spend against the model built during assessment.