Internal knowledge.
Powered by Amazon Bedrock.

A conceptual AI application with permission-aware retrieval, clear evaluation criteria and human oversight.

Illustrative AWS blueprints

These hypothetical architectures show a possible approach. They do not represent delivered customer projects or verified customer results.

Context & challenge

A hypothetical team wants a way to find answers in approved internal documents while keeping access aligned with source permissions.

Provide relevant answers that can point to source material, while controlling which documents each user can retrieve.

Proposed approach

Define the permitted document collection and access policy first. Retrieve relevant content for each request, generate an answer with sources, and retain human review for consequential uses.

Project scope

A project would define the implementation scope, responsibilities and acceptance criteria around the customer’s applications, data and operating requirements.

Example AWS architecture

Approved knowledgeAmazon S3
Retrieval & generationAmazon Bedrock
Application & reviewAccess / evaluation

Conceptual flow. Network boundaries, access, regions, scaling and recovery would be designed for the specific workload.

Amazon S3, Amazon Bedrock, AWS IAM, application authentication and a workload-specific retrieval layer. Final model, region and architecture are design decisions.

What a project would measure.

The measures below would establish whether the proposed design meets its objectives. Customer results would be published only with an agreed baseline, measured evidence and permission to share.

AreaEvidence to capture
Answer qualityA versioned evaluation set with source-grounded assessment.
Access boundariesTests confirming unauthorized documents cannot be retrieved.
Operational costObserved request volumes, token usage, latency and service charges.
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What would this look like for you?

Share your workload and business goals. We can explore the AWS architecture, cost decisions and next steps together.

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