AI Engineering / AWS

AI that works
beyond the pilot.

Put AI to work in the business. We connect knowledge, models, voice and workflows with clear controls and ownership.

Speak to a partnerExplore the architecture
REVOLEAP / AI ENGINEERINGContext becomes capability.

Useful answers. Verifiable sources.

Connect enterprise knowledge to models with access-aware retrieval and evaluation.

Amazon Bedrock · OpenAI · Governed knowledge
01 / The opportunity

Where can AI change the work?

Knowledge is hard to use.

Turn enterprise information into useful answers, with access tied to the user.

A pilot is hard to trust.

Define quality, latency and cost before the system reaches production.

The workflow needs to change.

Connect AI to real decisions and actions, with human review where it matters.

Our engineering position

Design the workflow. Then choose the model.

Start with the work, its consequences and the people who own it. Engineer the model into that system.

02 / In practice

Experience on both sides of the model.

Custom models. Managed AI. Choose the architecture around model fit, data boundaries, demand and the team that will operate it.

Self-managed compute

Amazon EC2

Run custom or open models with direct control over the inference runtime and compute.

ApplicationModel serverEC2 compute
Patterns
Real-time serving · Batch inference
Your team owns
Runtime, capacity, scaling and patching.
Kubernetes model serving

Amazon EKS

Deploy model servers as containers within an engineered Kubernetes platform.

ApplicationInference podsEC2 worker capacity
Patterns
vLLM serving · Pod & node scaling
Your team owns
Model serving, worker capacity and cluster operations.
Managed foundation models

Amazon Bedrock

Build with managed model access and application services for knowledge and actions.

ApplicationBedrock APIsFoundation model
Patterns
Model invocation · RAG · Agent workflows
Your team owns
Knowledge access, evaluation, workflow controls and usage.
These paths can work together.

EKS can host an application that calls Bedrock, or serve a model on EC2 workers. Choose each layer for its operating needs.

Where managed custom-model endpoints fit

Amazon SageMaker AI

A further AWS pattern for teams that need custom model deployment with managed endpoints. Evaluate it alongside self-managed compute when endpoint operations and deployment controls matter.

AWS architecture guidance: Inference stack · Model serving on EKS · RAG and agent patterns

Production agents on AWS

Amazon Bedrock AgentCore

Evaluate managed agent runtime, identity, tool access and observability when your agent needs a production operating layer. Choose the model and framework around the task.

AWS architecture guidance
Our model partnership

OpenAI, within the system.

Build with OpenAI where its models fit the workflow. Keep application permissions, enterprise integrations, evaluation and cloud operations explicit across the architecture.

Our OpenAI approach
03 / The engineering

Useful AI needs a complete system.

Business

Own the decision.

Workflow value, acceptance criteria and human oversight.

Engineering

Ground the system.

Knowledge access, model orchestration and controlled actions.

Operations

Keep it dependable.

Evaluation, telemetry, cost and accountable service owners.

Explore the four levels of AI adoption
Assisted

AI helps people complete individual tasks.

Augmented

AI supports an existing business workflow.

Architected

The workflow is redesigned around AI, with explicit controls and ownership.

Autonomous

Defined work runs within boundaries set by people.

Inside four AI workflows Architecture & controls
REVOLEAP / AI ENGINEERING

AI, in the flow of work.

Explore a scenario
Business intent

“What does our policy allow?”

UnderstandFind the answer in approved knowledge.
EngineerAmazon Bedrock Knowledge Bases

Retrieve from indexed enterprise documents.

DeliverA cited answer, within the user’s access.
Designed around

Source freshness · User permissions · Retrieval quality

Illustrative workflows. Shaped around your business.AWS / AI
Voice AI / ElevenLabs agent platform

A natural conversation.
A useful business outcome.

Connect the conversation to the work behind it. We engineer ElevenLabs agents around your customer journeys, enterprise tools and service teams.

Customer context

CRM & service tools

Salesforce · HubSpot · Zendesk
Your own business applications

Customer channels

Telephony & contact centre

Amazon Connect · Twilio · SIP
Web and mobile experiences

Customer infrastructure

Your tools, wherever they run.

AWS · Other clouds · On-premises
Authenticated APIs and controlled connectivity

Discuss a voice workflow
ElevenLabs / Dialogue + actionIllustrative service journey
  1. Customer

    “Could we move my service visit to Friday?”

  2. ElevenLabs agent

    “I can help. Let’s verify your booking first.”

  3. Authenticated tool / AWS exampleCheck booking & availability

    API Gateway + Lambda connect to the scheduling system.

  4. ElevenLabs agent

    “Friday at 10 is available. Shall I confirm that?”

  5. Customer

    “Yes, please.”

  6. Confirmed actionBooking updated. CRM record synchronised.

    A confirmation is returned to the conversation.

Human help stays in the journey.

Route exceptions to a service agent, with conversation context and the actions already taken.

Integration patterns: ElevenLabs integrations · Agent tools · Amazon Connect

What success looks like

Agree the outcome.
Engineer the evidence.

The measures depend on your starting point. We define them with you before delivery begins.

Useful behaviour

Task completion, grounded answers and appropriate escalation against an agreed evaluation set.

Workable economics

Response time and cost per completed task, measured with representative demand.

A service you own

Access controls, action traces, release tests and a team ready to operate the system.

04 / How we engage

The right depth of involvement.

Understand

Find the useful opportunity.

Assess the workflow, data readiness and the value worth pursuing.

An opportunity and risk brief.
Design & deliver

Build the working system.

Engineer and evaluate the service alongside your team, against agreed acceptance criteria.

A working service. A release decision.
Retain & evolve

Develop lasting capability.

Improve quality and economics as your team takes ownership of the next phase.

Evaluation practices and an operating cadence.
The supporting practices

The foundations
come with the thinking.

AI Engineering connects to the four disciplines that turn an architecture into a service your business can run.

Cloud EngineeringThe infrastructure beneath the service.Data EngineeringThe knowledge and signals it depends on.App EngineeringThe interfaces where people use it.Platform OperationsThe discipline that keeps it working.
Where to start

Let’s start with the layer
that’s holding you up.

Bring a business workflow or an AI pilot. Leave a 90-minute working session with clearer priorities and a practical next step.

Begin a conversationThe Leap Assessment / 90 minutes