AWS organisations adopting generative AI
Use existing cloud identity, networking, storage, data, monitoring, security, and operational practices around AI workloads.
Foundation models, agents, knowledge bases, guardrails, evaluations, AWS integrations, security, observability, and operations
Amazon Bedrock development services
Rokad develops enterprise generative AI applications on Amazon Bedrock across foundation models, agents, knowledge bases, guardrails, evaluations, and AWS integration.
Platform fit / 01
Designed for teams with a specific platform requirement.
Amazon Bedrock provides managed access to foundation models and services for agents, knowledge bases, guardrails, and evaluation within AWS. Rokad designs account, region, identity, data, model, retrieval, tool, security, application, observability, and cost architecture around the business task.
Teams requiring managed multi-model access
Evaluate and route foundation models while keeping application, data, permissions, evaluation, and operations consistent.
Enterprises building knowledge and agent systems
Combine Knowledge Bases, agents, guardrails, AWS services, business APIs, approvals, and audit.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
Model choice is separated from application evaluation
Teams enable several models without task datasets, routing criteria, version controls, cost models, or fallback behaviour.
Knowledge Bases are treated as automatic accuracy
Chunking, metadata, filters, source quality, retrieval, citations, access, freshness, and evaluation are not engineered.
AWS permissions become overly broad
Agents, functions, data, storage, search, secrets, logs, and application services share roles without least-privilege boundaries.
Platform capabilities / 03
What Rokad can implement and operate.
01
Amazon Bedrock use-case, model, region, account, data, security, cost, and architecture assessment
02
Foundation model access, inference profiles, routing, evaluation, versioning, quotas, latency, and fallback
03
Bedrock Agents, action groups, APIs, functions, state, approvals, audit, and application integration
04
Knowledge Bases, ingestion, chunking, metadata, retrieval, reranking, citations, and RAG evaluation
05
Guardrails, policy, content controls, sensitive data handling, application safeguards, and human review
06
IAM, VPC, KMS, S3, Lambda, search, databases, logging, monitoring, and AWS service integration
07
Deployment, CI/CD, observability, model evaluation, cost, support, governance, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
AWS AI foundation
Accounts, regions, IAM, network, encryption, storage, data, logging, secrets, quotas, billing, and deployment.
Model and agent layer
Models, inference, prompts, agents, action groups, sessions, routing, versions, guardrails, and fallback.
Knowledge system
Sources, ingestion, parsing, chunking, metadata, embeddings, retrieval, reranking, citations, access, and freshness.
Evaluation and operation
Model and RAG evaluation, traces, logs, quality, safety, latency, cost, incidents, releases, and governance.
Use cases / 05
Where this platform creates practical leverage.
AWS enterprise knowledge assistant
Use governed documents and data with Knowledge Bases, citations, identity, application context, and escalation.
Bedrock business agent
Connect agents to AWS and enterprise tools with action groups, permissions, validation, approvals, and audit.
Multi-model AI application
Route tasks across available models using quality, modality, latency, cost, region, and governance criteria.
Document and workflow intelligence
Extract, classify, summarise, validate, retrieve, route review, and update business systems on AWS.
Architecture / 06
Platform-specific engineering decisions and boundaries.
IAM follows every tool and data boundary
Give agents, functions, applications, ingestion, retrieval, and operations separate scoped roles and audit trails.
RAG is evaluated as a pipeline
Measure source quality, parsing, chunks, metadata, retrieval, ranking, citations, response, latency, and cost together.
Model access remains replaceable
Keep task contracts, evaluation, prompts, schemas, tools, and application logic sufficiently separated from model-specific behaviour.
Quality and governance / 07
Production controls are part of the implementation.
Evaluated behaviour
Representative datasets, task criteria, failure modes, model comparisons, and release thresholds are defined before production expansion.
Governed model access
Identity, data boundaries, tool permissions, moderation, approvals, audit, retention, and provider controls match the use case.
Provider-aware operation
Models, prompts, tools, latency, cost, quotas, versions, fallbacks, telemetry, and migration risk are monitored explicitly.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Bedrock model, account, region, data, IAM, security, cost, and risk assessment
Model, agent, Knowledge Base, guardrail, AWS integration, evaluation, and operating architecture
Production Bedrock applications, agents, action groups, knowledge systems, and workflows
AWS identity, network, storage, search, data, functions, APIs, logging, and application integrations
Model and RAG evaluations, guardrails, monitoring, latency, cost, deployment, and release controls
Developer, cloud, security, operator, governance, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Microsoft Foundry
Azure platform for models, agents, enterprise tools, tracing, monitoring, and evaluations.
02
Google Gemini
Gemini multimodal applications and Vertex AI deployment on Google Cloud.
03
Hugging Face
Open model discovery, evaluation, custom inference, fine-tuning, and model application tooling.
01
AI development
AI applications, agents, retrieval, evaluation, model integration, and intelligent workflows.
02
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
03
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
FAQ
Amazon Bedrock development services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Can Rokad help select a model in Amazon Bedrock?
Can Rokad help select a model in Amazon Bedrock?
Yes. We evaluate representative tasks across quality, modality, tools, context, latency, cost, region, quotas, data controls, and operational fit.
02Can Bedrock connect to private documents?
Can Bedrock connect to private documents?
Yes. We can implement Knowledge Bases or custom retrieval with source access, ingestion, metadata, chunking, citations, permissions, freshness, and evaluation.
03Can Rokad build Bedrock agents?
Can Rokad build Bedrock agents?
Yes. We design action groups, APIs, functions, permissions, state, validation, approvals, guardrails, audit, evaluation, and application integration.
04Can Rokad manage Bedrock applications after launch?
Can Rokad manage Bedrock applications after launch?
Yes. Managed services can cover models, agents, knowledge ingestion, evaluations, monitoring, security, AWS infrastructure, costs, incidents, and releases.
Amazon Bedrock · AI integration services
Build Bedrock applications with AWS-grade identity, data, evaluation, and operations.
Rokad can select models, build agents and knowledge systems, integrate AWS services, and establish guardrails and production controls.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.