Upvantix

AWS Data & AI Delivery

Working AI in 5 days. One production use case on AWS in 30.

Upvantix combines AI, data, integration, security and cloud engineering to deliver a focused POC and take the validated use case into production on AWS.

Assess an AWS AI use case Explore AI services

The 5/30 AI Delivery Commitment

5

Business days

Working AI POC

30

Business days

Production use case on AWS

Built to scale and operate after launch.

How 5/30 runs on AWS

From a working AI proof to a production workload on AWS.

Use AWS AI, data, integration, security and operations services according to the approved architecture and business outcome.

Prove5 business days

5-Day AI POC

Validate the workflow, data, quality and technical feasibility before approving a production build.

  • Working AI experience
  • Customer-provided data sample
  • Initial model and output evaluation
  • AWS production architecture recommendation

Output

Working AI POC + Production Recommendation

Assess a 5-Day AI POC
Build30 business days

30-Day Production Use Case

Turn the validated POC into one secure, integrated and observable production capability within the approved scope.

  • Production workload on AWS
  • Agreed enterprise integrations
  • Security, guardrails and approvals
  • Monitoring and operational handover

Output

Production AI Application or Agentic Workflow

Discuss a production use case

Technology foundation

Deep AI engineering, built on AWS.

Depending on the use case, Upvantix combines appropriate AWS AI, data, application and cloud services with enterprise systems and custom engineering. Technology follows the architecture—not the other way around.

Explore the AWS data foundation
AWS

Generative AI

Foundation models and managed capabilities for generative AI applications.

Amazon Bedrock

AWS

Agentic AI

Agent runtimes, orchestration patterns and tools selected for the workflow.

Amazon Bedrock AgentCore

Strands Agents

Amazon Bedrock Agents

AWS

Machine Learning

Model development, training, evaluation and deployment capabilities.

Amazon SageMaker AI

AWS

Enterprise Knowledge

Retrieval and search foundations for grounded enterprise AI experiences.

Amazon Bedrock Knowledge Bases

Amazon OpenSearch Service

AWS

Guardrails

Configurable safeguards aligned with the application and risk context.

Amazon Bedrock Guardrails

AWS

Data

Storage, analytics, integration and database services behind the AI workload.

Amazon S3

Amazon Redshift

AWS Glue

Amazon Aurora

AWS

Integration & Compute

Application, event and compute layers for connecting AI to enterprise systems.

AWS Lambda

Amazon ECS / EKS

Amazon API Gateway

Amazon EventBridge

AWS

Security

Identity, encryption and secrets controls that support the target architecture.

AWS IAM

AWS KMS

AWS Secrets Manager

AWS

Observability

Operational signals for applications, infrastructure and AI workflows.

Amazon CloudWatch

Engineering principles

AWS is the foundation. The workflow is the design center.

01

Outcome before service

Begin with the business workflow, expected result and operating constraints.

02

Fit-for-purpose models

Select models and techniques according to quality, latency, cost and risk needs.

03

Enterprise integration

Connect securely to the data and applications where the work already happens.

04

Operate from day one

Design evaluation, observability, security and lifecycle management into the system.

The 5/30 AI Delivery Commitment

Bring us one qualified AWS AI use case.

We will assess its fit for the 5/30 delivery model and define the data, access, AWS architecture and production scope required to begin.

Assess an AWS AI use case Explore AI/ML services