Resources
Documentation and guides
Find the existing procurement, account and deployment guidance in one place.
Procurement and account arrangements
Review how requirements are scoped, or contact us about an enterprise account.
Models and cloud resources
Use the model catalogue and service pages to identify the resources you need. Specific availability is confirmed in the proposal.
Deployment and planning guides
Data Residency and Routing: Verifying Where Data Is Processed
Trace endpoints, inference routes, storage, logs and support access. Verify failover and evidence rather than treating an endpoint region as a compliance conclusion.
Getting Enterprise RAG to Production Quality
Why retrieval demos look great and production RAG disappoints — and the steps that close the gap.
LLM Cost Governance: What to Actually Measure
A practical view of the few metrics that keep model spend predictable as usage scales.
Why AI Projects Stall Between Demo and Production
The few steps between a working demo and a live system — and how to plan for them up front.
Cloud API integration guides
Amazon Bedrock API: GPT and Claude integration
Configure Runtime and Mantle, verify access, send SDK/HTTP requests and handle streaming, errors, cost and data retention.
Microsoft Foundry and Azure OpenAI API setup
Configure a deployed model, v1 endpoint, identity, streaming and usage checks.
Alibaba Cloud Model Studio API setup
Match workspace, region and plan, then parse streaming text and usage safely.
Tencent TokenHub API setup
Match site and region, discover online model IDs, and verify chat, streaming and usage.
BytePlus ModelArk API setup
Confirm regional activation, Bearer authentication, Chat streaming and processing boundaries.
Architecture and delivery guides
Amazon Bedrock in an enterprise architecture
Define the model service, application responsibilities and evidence needed before a pilot.
Choose a Bedrock model with a task evaluation
Compare quality, latency, permissions and cost on the same workload.
Eight decisions from a Bedrock pilot to production
Connect each implementation step to an owner, artifact and acceptance check.
Assess readiness for a Bedrock pilot
Turn business, data, account and operational gaps into a prioritized pilot scope.
Build and evaluate a Bedrock knowledge workflow
Prepare sources, retrieval permissions, citations and separate retrieval from answer evaluation.
Diagnose a failing RAG workflow
Use observable evidence to separate source, ingestion, retrieval, permission and generation failures.
Decide when a business workflow needs an agent
Compare an agent with a deterministic workflow and define the action contract before integration.
Move from a chatbot to controlled business actions
Add retrieval, read-only tools and approved writes in separate, testable stages.
Place Guardrails within an application safety boundary
Separate content checks from document authorization and business-action controls.
Review Bedrock data, identity and operational controls
Collect concrete evidence for permissions, processing locations, storage and incident ownership.
Build a Bedrock cost baseline before launch
Estimate accepted-task cost with token categories, retries, retrieval and operating resources.
Six workflows to evaluate with Amazon Bedrock
Use six concrete workflow patterns to define inputs, outputs and acceptance boundaries.
Start with the resources you need.
A product list, preferred region and estimated usage are enough to begin a conversation.
Discuss your requirements