What can we build with Amazon Bedrock?

Assistants grounded in your documents, summarisation and drafting tools, agents that complete multi-step tasks, and search experiences that explain their answers, all using managed foundation models.

When should we use SageMaker instead of Bedrock?

Use Bedrock when a general model with your content is enough. Use SageMaker AI when you need a model trained on your own data for prediction, ranking or classification, or full control over training and hosting.

Can AI process our documents and forms?

Yes. Textract extracts text, tables and forms, Comprehend classifies and redacts content, and a workflow with human review handles the uncertain cases, feeding clean data into your systems.

Can you build recommendations or forecasting?

Yes. We use Personalize for real-time recommendations, and custom SageMaker models for demand, risk or maintenance predictions where your data justifies it.

Can this work with our existing apps?

Yes. We expose AI features through secure APIs and events, so your web, mobile and back-office applications can call them without changing how they are built.

Which models are available on Bedrock?

Bedrock offers a selection of foundation models from several providers through one API. The available models and regions change over time, so we check current availability when we design your solution.

How do you reduce hallucinations?

We ground answers in your own content with retrieval, add guardrails, require citations, and measure quality with test sets before launch and continuously after.

Is our data used to train the public models?

With Bedrock, your prompts and data are not used to train the underlying foundation models by default, and you control encryption and access. We confirm the current terms for your account during design.

How do you handle infrastructure?

Everything is defined as code with Terraform, CDK or CloudFormation, deployed through pipelines, so environments are repeatable, reviewable and easy to hand over.

Can we run models in a private network?

Yes. We use VPC endpoints and PrivateLink so AI traffic stays on the AWS network, together with KMS encryption and tight IAM roles.

How is our data protected?

Encryption in transit and at rest with KMS, least-privilege IAM, private networking, logging and alerting. Security is designed in from the first architecture review, not added at the end.

Can you support regulated industries?

We design to align with the regulations that apply to you, such as HIPAA or GDPR, using access controls, audit logging, data minimisation and region choice. Your compliance team stays involved throughout.

Who is responsible for what?

AWS secures the cloud platform. Aarvexa designs and configures the AI solution securely on top of it. You own your data, policies and approvals. We document this split clearly in every engagement.

Can we choose the AWS region?

Yes. We select regions to meet your data residency, latency and service-availability needs, and document the trade-offs.

Do we own what you build?

Yes. Code, infrastructure definitions, prompts, evaluation sets and documentation live in your accounts and repositories, so you are never locked in to Aarvexa.

How long does a first AI project take?

A focused pilot typically takes four to nine weeks, depending on the use case and data readiness. Custom model work takes longer than managed-service builds.

How much will AWS cost to run?

It depends on volume, models and architecture. We build a cost model during assessment and keep it current, and we design for usage-based pricing so a pilot stays small.

How do you keep costs under control?

Budgets and alerts, tagging, savings plans, right-sized endpoints, model routing, caching and storage tiering, reviewed monthly once you are in production.

Can we start with a small pilot?

Yes, and we recommend it. A pilot with clear success metrics proves value, exposes data issues early, and gives you a realistic basis for scaling.

Do you offer ongoing support?

Yes. We provide managed operations, monthly cost and quality reviews, and a roadmap for the next use cases, on a flexible monthly engagement.

AI Services in AWS

Production AI on AWS, built right

Aarvexa designs, builds and runs generative AI, machine learning and intelligent automation on Amazon Web Services, secure by design, cost-aware, and shipped as code.

Bedrock & SageMaker
Secure landing zone
Cost-aware design

Book a free architecture review See architectures

Data

OpenSearch

DynamoDB

AI & ML

Bedrock

Lambda

Guardrails

Experience

Your app

API Gateway

Auth

request tracelive

01POST /askYour app0 ms
02AuthenticatedAPI Gateway11 ms
03OrchestrateLambda34 ms
04Retrieve contextBedrock KB86 ms
05Vector searchOpenSearch112 ms
06Fetch passagesS3131 ms
07Generate + guardBedrock640 ms
08200 OK · cited answerAPI Gateway702 ms

Guardrails

Policy checked

This request

$0.004 · serverless

AWS AI toolbox

The right AWS service for each job

Pre-trained APIs for speed, foundation models for flexibility, and SageMaker for full control. Filter to explore.

Generative AI

Amazon Bedrock

Foundation models behind one API, with guardrails, knowledge bases and agents.

Assistants, summarisation, content

Generative AI

Bedrock Agents

Agents that plan tasks and call your APIs, with memory and approvals.

Workflow automation, copilots

Machine learning

Amazon SageMaker AI

Build, train, tune and deploy custom models with managed infrastructure.

Prediction, ranking, fine-tuning

Machine learning

Amazon Personalize

Real-time recommendations from your own interaction data.

Retail, media, content feeds

Vision & language

Amazon Rekognition

Image and video analysis: objects, faces, text and content moderation.

Safety, KYC, media tagging

Vision & language

Amazon Textract

Extracts text, tables and forms from scanned documents.

Invoices, claims, onboarding

Vision & language

Amazon Comprehend

Entities, sentiment, topics and PII detection in text.

Feedback, compliance, routing

Vision & language

Transcribe & Polly

Speech to text, and lifelike text to speech, in many languages.

Call analytics, voice apps

Vision & language

Amazon Lex

Conversational interfaces for chat and voice, tied to your back end.

Support bots, IVR

Data & search

Kendra & OpenSearch

Enterprise and vector search that grounds AI answers in your content.

RAG, knowledge portals

Data & search

S3, Glue & Athena

A governed data lake with catalogs, ETL and serverless SQL.

Training data, analytics

MLOps & security

Lambda & Step Functions

Serverless glue that orchestrates pipelines and inference calls.

Event-driven AI workflows

MLOps & security

IAM, KMS & PrivateLink

Least-privilege access, encryption and private network paths for AI traffic.

Regulated workloads

MLOps & security

CloudWatch & Model Monitor

Metrics, logs and drift detection for models in production.

Reliability, quality tracking

Amazon Web Services and service names are trademarks of Amazon.com, Inc. or its affiliates. Aarvexa is an independent consultancy; service availability varies by region.

Reference architectures

Proven blueprints, tuned to your data

Choose a use case to see how the AWS services connect. Every blueprint is deployed as code.

An assistant that answers from your documents

Users ask in natural language. The assistant retrieves the right passages from your content and writes a grounded, cited answer.

UsersWeb & chat

API GatewayAuth & throttling

LambdaOrchestration

BedrockModel + guardrails

OpenSearchVector index

S3Source documents

Answers are grounded in your content, with sources shown
Guardrails block unsafe or off-topic output
Fully serverless, so cost follows usage

Find your path

Which AWS AI path fits you?

Three quick questions and we will suggest where to start. No sign-up needed.

Question 1 of 3

What are you trying to build?

Your recommendation appears here

Answer the three questions to see a suggested stack, timeline and first steps.

Infrastructure as code

Every environment, one command away

Your AI platform is defined in code, reviewed like any change, and deployed the same way every time.

main.tf

1resource "aws_s3_bucket" "docs" {
2  bucket = "acme-ai-documents"
3}
4
5resource "aws_kms_key" "ai" {
6  enable_key_rotation = true
7}
8
9resource "aws_lambda_function" "api" {
10  runtime = "python3.12"
11  handler = "app.handler"
12}
13
14resource "aws_bedrock_guardrail" "safe" {
15  name = "acme-guardrail"
16}

terminal

$terraform apply

Well-Architected review

Measured against the six pillars

Every solution is reviewed against the AWS Well-Architected Framework. Toggle to compare a typical starting point with the result of our review.

Operational excellence
Runbooks, deployments as code, and observability that make changes routine.
Security
Least privilege, encryption everywhere, and private paths for AI traffic.
Reliability
Multi-AZ design, retries, back-pressure and tested recovery.
Performance efficiency
Right-sized compute, caching and the right model for each task.
Cost optimization
Tagging, budgets, savings plans and usage-based architecture.
Sustainability
Efficient instances, serverless where it fits, and lean data retention.

Illustrative scoring for explanation. Real reviews use the official AWS Well-Architected Tool and your own workload data.

Security & compliance

Security is shared, so we make the split explicit

Three layers, three owners. We document exactly who does what before a single resource is created.

You own

Your data, your decisions

Data classification & retention
Which use cases to approve
Who gets access
Policies and sign-off

Aarvexa designs and configures

Everything built on top of AWS

IAM, KMS and network design
Guardrails and prompt safety
Pipelines, monitoring and alerts
Runbooks and handover

AWS secures the cloud

The platform underneath

Data centers and hardware
Managed service infrastructure
Global network
Platform compliance programs

Designed to align withGDPRHIPAASOC 2ISO 27001PCI DSS

Industries

AI that fits how your industry works

Hover a tile to see typical use cases.

Healthcare & life sciences

Clinical documentation, imaging triage and patient engagement on privacy-first architecture.

Clinical note summaries
Prior-auth automation
Medical coding assist

Financial services

Document intake, risk signals and compliant assistants.

KYC document checks
Fraud features
Advisor copilots

Retail & e-commerce

Personalization, search and demand insight.

Recommendations
Visual search
Support bots

Manufacturing

Predictive maintenance and visual quality control.

Defect detection
Downtime prediction
Shift summaries

Logistics

Route, ETA and paperwork automation.

Bill of lading OCR
ETA models
Exception alerts

Media & education

Content tagging, localization and tutoring.

Auto-tagging
Dubbing & captions
AI tutors

Cost optimization

Switch on the savings, watch the bill shrink

An illustrative simulator for a typical AI workload. Real savings depend on your usage, which is why we model it first.

Current monthly AWS AI spend$48,000

Estimated monthly bill$40,800

Saving $7,200 a month · 15%

Compute$16,128
Storage & data$7,392
Managed AI APIs$9,600
Data transfer$3,840
Other$3,840

Get a real cost model

Delivery roadmap

From first call to production in about twelve weeks

Scroll through the phases. Each one ends with something you can see, test and approve.

01
Weeks 1 to 2
Assess
We map use cases, data and risks, and score each idea on value and feasibility, so the first build is the right one.
- Use-case shortlist
- Data readiness report
- AWS cost model
02
Weeks 2 to 4
Foundation
A secure landing zone with accounts, networking, identity and guardrails, built as code from the first commit.
- Landing zone as code
- Identity & access model
- Logging & tagging
03
Weeks 4 to 9
Pilot
A working slice of the solution in front of real users, evaluated against agreed quality and cost targets.
- Pilot in production-like setup
- Evaluation report
- Go / no-go decision
04
Weeks 9 to 12
Harden
Load testing, security review, monitoring, runbooks and a rehearsed recovery plan before wider launch.
- Security review
- Dashboards & alerts
- Runbooks
05
Ongoing
Scale & optimise
Roll out to more teams, tune cost every month, and keep improving the models with real feedback.
- Monthly cost review
- Model improvements
- Roadmap for next use cases

FAQ

Frequently Asked Questions

Straight answers on AI services in AWS: architecture, security, cost and timelines.

They are the managed machine learning and generative AI services on Amazon Web Services, such as Amazon Bedrock, SageMaker AI, Rekognition, Textract, Comprehend and Personalize, which you combine with data and security services to build AI solutions without running everything yourself.

We design, build and run AI solutions on AWS: choosing the right services, setting up a secure foundation as code, building and evaluating the solution, and handing over with monitoring and documentation.

No. Many clients start with a small AWS footprint for the AI workload and connect it to existing systems. We also help plan the landing zone, so the first project does not become a security or cost problem later.

Product and engineering teams adding AI features, operations teams automating document-heavy work, and leaders who want a clear, costed plan before committing budget.

Book a free consultation. We review your goals and data, recommend an architecture, and propose a small pilot with measurable success criteria.

Start on AWS

Ready to put AI to work on AWS?

Book a free architecture review. We will look at your use case, your data and your AWS setup, and show you a costed plan for the first release.

Book a free architecture review Find my path