Cloud & Infrastructure

GenAI on AWS (Amazon Bedrock)

Production generative AI on AWS, RAG, agents, guardrails, and evals on Bedrock.

What We Deliver

We build generative AI on your own AWS account using Amazon Bedrock and SageMaker, RAG systems, agents, and LLM features that stay inside your VPC, respect your data boundaries, and run under real guardrails. Not a demo wired to someone else’s API, but a production system you own.

Bedrock model selection and access setup
RAG pipeline with a managed vector store
Agents and tool integrations with guardrails
Evaluation harness and output monitoring
Cost, latency, and throughput controls
IAM, VPC, and data-residency alignment

When You Need This

You want to ship real generative AI but cannot send your data or your customers’ data to a third-party API, because of compliance, contracts, or data-residency rules. Or you built a proof of concept on a public API and now need it to run in production, inside your security boundary, with cost and quality under control. If AI matters to your product and the data cannot leave your account, building on Bedrock in your own AWS is the path from demo to production system.

How We Approach It

Scope the use case and data

We pin down what the system must do and what data it can touch, so the design respects your residency and compliance constraints from the start.

Set up Bedrock in your account

Model selection and access, a RAG pipeline with a managed vector store, and agents with tool integrations, all running inside your VPC and IAM.

Guardrail and evaluate

Bedrock Guardrails plus your own validation, and an evaluation harness that measures the system against real tasks, not vibes, before it reaches users.

Control cost and operate

On-demand, batch, or provisioned to fit spend, with latency and throughput controls and output monitoring, so it runs reliably as a system you own.

Why This Matters

The Difference It Makes

Native to AWS

Runs in your account, inside your security boundary.

Guardrailed

Bedrock Guardrails plus your own validation.

Evaluated

Measured against real tasks, not vibes.

Cost-Controlled

On-demand, batch, or provisioned to fit spend.

Our Toolkit

Technologies We Use

AWS BedrockSageMakerAmazon NovaAnthropic Claudepgvector / OpenSearch

FAQ

Common Questions

Why build GenAI on Bedrock instead of a plain API?

Bedrock keeps model calls inside your AWS account and security boundary, gives you a unified API across model providers, and adds native guardrails, which matters when data residency or compliance is on the line.

Bedrock or SageMaker?

Bedrock for serverless access to foundation models and fast RAG/agent builds; SageMaker when you need fine-tuning or to host your own models. We pick per use case and often combine them.

Can you keep our data private?

Yes, the system runs in your VPC with your IAM and data-residency rules, and prompts/outputs are not shared outside your account.