Model Layer
Amazon Bedrock with deliberate model choice (Claude, Nova, Llama) based on workload requirements. Provisioned Throughput or on-demand based on traffic patterns.
Generative AI on AWS
Amazon Bedrock, Deployed for Production
Most enterprise GenAI initiatives stall in the gap between prototype and production. The model works. The demo impresses. Then the questions start: how is the data protected, what does inference actually cost at scale, who audits the outputs, and what happens when the model version changes.
We build Amazon Bedrock deployments that answer those questions before they stall your project.
AWS Advanced Consulting Partner. Bedrock deployed in production across our Aegis CX and InsightOps services.
.png)
.png)
.png)
.png)
Production-Ready GenAI
Bedrock as a Production AWS Workload
We treat Amazon Bedrock deployments the same way we treat any other production AWS workload. That means designing for observability, cost control, security, and graceful version management from day one.
The Reality: Prototypes Work Until Production Forces the Hard Questions
Almost every enterprise we work with has an Amazon Bedrock prototype running somewhere. The prototypes work because the hard parts were bypassed.
Data governance and permission boundaries become complex at scale
Model outputs need policy compliance without brittle rules engines
Inference costs multiply unpredictably with usage growth
Model version changes require migration strategies, not coin flips
Audit requirements demand full traceability without exploding costs
Our Approach: Production-Grade Architecture
A typical production Bedrock deployment we build includes five integrated layers designed for enterprise governance and operational reliability.
Retrieval Layer
Bedrock Knowledge Bases with OpenSearch Serverless or Kendra, plus ingestion pipelines from your source systems.
Orchestration Layer
AWS Lambda, Step Functions, and API Gateway for request handling. Bedrock Agents only where tool use is genuinely warranted.
Governance Layer
Bedrock Guardrails for content policies, KMS encryption, CloudTrail audit logging, and CloudWatch cost visibility.
Evaluation Layer
Test harnesses for model version comparison with measurable output quality scoring. Model upgrades become data-driven decisions.
How We Deliver
Four-phase approach from use case qualification through production operations.
1
Use Case Qualification
1-2 weeks. Workshop and data review. If the use case doesn't fit Bedrock, we say so and don't charge for a build we don't believe in.
2
Architecture and Foundation
2-4 weeks. Target architecture, AWS foundation integration, data pipeline design, and governance model.
3
Build and Iterate
6-12 weeks. Iterative build with weekly demos, evaluation-driven tuning, and business process integration.
4
Operate
Production handoff with runbooks and monitoring. Ongoing operations under Aegis if selected.
What This Engagement Covers
Complete production deployment from architecture through operational handoff.
Architecture Design
Target architecture covering model selection, retrieval design, orchestration, governance, and cost envelope with documented rationale.
Full Implementation
Bedrock, Knowledge Bases, Lambda integration, Guardrails configuration, and observability stack with CI/CD pipeline.
Evaluation Infrastructure
Test-driven evaluation tooling for comparing model versions, prompt changes, and retrieval tuning with measurable quality metrics.
Outcomes
- Production-ready GenAI with enterprise governance
- Predictable inference costs with usage-based monitoring
- Data-driven model version management
- Full audit trail for compliance requirements
- Measurable output quality over time
Ideal Fit
- Organizations past the Bedrock prototype phase needing production deployment
- Enterprises with governance or data sensitivity requirements
- Teams wanting AWS-native GenAI rather than external APIs
- Organizations with engineering capacity to co-own the deployment
- Companies willing to invest in evaluation infrastructure over trust-by-default
Use Case Fit
When Amazon Bedrock is the right choice for enterprise GenAI
Not every problem is a GenAI problem. We assess whether your target use case is well-matched to Bedrock or if alternative approaches would deliver better outcomes.
Recommended
Knowledge Base Search
High Fit
Document retrieval and Q&A against enterprise content repositories.
Best Fit
Large document collections, complex queries requiring reasoning, regulated industries needing data control.
Recommended
Customer Service Automation
High Fit
Intelligent response generation for support tickets and customer inquiries.
Best Fit
High-volume support environments, complex product catalogs, need for consistent brand voice.
Content Generation
Medium Fit
Marketing copy, documentation, and internal communications.
Best Fit
Structured content workflows, brand compliance requirements, human review processes.
Why IVI
Built by engineers who run Bedrock in production
We run Bedrock in production, not just for customers
Bedrock powers automation in our Aegis CX service, drives analytics in InsightOps, and supports internal business processes.
Production Reference
The patterns we recommend are tested at our own expense across multiple production workloads.
AWS-native by default
Built with native AWS services for governance, observability, and cost control that work together seamlessly.
Integrated Stack
Bedrock, Lambda, API Gateway, OpenSearch, KMS, CloudTrail, CloudWatch - components that speak the same language.
Heading
A popup in HTML refers to a small window that appears on top of a web page. It's commonly used to display additional information, alerts, or interactive content without navigating away from the current page.
Related Resources
Related Resources
Review related solution pages, supporting materials, and additional resources that help explain where this solution fits and how it can be applied.
FAQs
Frequently Asked Questions
Common questions about Amazon Bedrock implementation for enterprise GenAI.
Why Amazon Bedrock instead of OpenAI or Azure OpenAI?
Usually the answer is data control. With Bedrock, your prompts and retrieved context never leave your AWS account. Model invocations are logged to your CloudTrail, encrypted with your KMS keys, and bounded by your IAM policies. For regulated industries and enterprises with strict data governance, that boundary matters more than which specific model performs 2% better on a given benchmark.
Which model should we use?
It depends on the workload. Anthropic Claude models are strong for long-context reasoning and document analysis. Amazon Nova models offer cost-efficient performance for high-volume workloads. Meta Llama models are useful for specific fine-tuning scenarios. Model selection is part of the architecture phase and is based on measured performance against your specific use case, not a generic recommendation.
How do you handle model version changes?
With an evaluation harness built during the initial engagement. When Anthropic or Amazon releases a new model version, we run your representative prompt set through both versions and measure output quality against scored criteria. You see the delta before you cut over, so model upgrades stop being a coin flip.
What about hallucination and accuracy?
Hallucination is managed, not eliminated. We architect retrieval-augmented generation so the model's answers are grounded in your content, tune Knowledge Base retrieval aggressively, use Bedrock Guardrails to enforce content policies, and build evaluation tooling that measures answer quality over time. For high-stakes outputs, we architect human-in-the-loop review rather than pretending the model is reliable on its own.
What does this actually cost to operate?
Highly variable based on model choice, token counts, and usage volume. A well-architected production deployment for a mid-market enterprise typically runs in the low thousands to low tens of thousands per month in AWS infrastructure costs, excluding services. We model expected cost during the architecture phase and instrument real cost visibility from day one.
Do we need our data on AWS to use this?
Your source content needs to be ingestible into AWS. For most enterprises that means connecting Bedrock Knowledge Bases to S3, SharePoint, Confluence, or a database. The ingestion pipeline keeps your source of truth in place; only the indexed content lives in your AWS account.