AWS & DevOps services

Intelligent Automation

Automate document, operations, and knowledge workflows by combining deterministic services, AWS AI capabilities, human review, and observable control points.

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Scope

What this service can cover

The exact implementation follows the environment and the signed scope. These are the technical workstreams most often composed for Intelligent Automation.

01

Workflow discovery

Map inputs, decisions, systems, exceptions, data sensitivity, volumes, owners, and the steps that should remain deterministic.

02

Service composition

Combine event, document, search, model, API, and workflow services around explicit contracts and failure paths.

03

Human control

Define review queues, confidence or policy thresholds, overrides, approvals, traceability, and escalation.

04

Production operation

Implement infrastructure, deployments, telemetry, cost limits, replay, testing, and an owned improvement backlog.

Work products

What the client receives

  • Workflow and decision-boundary map
  • Automation architecture and implementation
  • Test, review, and exception controls
  • Runbooks, metrics, and handoff
Delivery approach

From current state to client-owned handoff

01

Qualify

Define the user, decision, source data, expected output, failure cost, and whether AI or deterministic automation fits.

02

Bound

Set data, identity, model, tool, review, security, cost, and observability boundaries before implementation.

03

Build and evaluate

Implement the workflow with representative tests, explicit quality criteria, failure paths, and controlled deployment.

04

Operate and hand off

Transfer code, prompts, evaluation assets, runbooks, budgets, monitoring, rollback, and accountable ownership.

Ways to engage

Match the commercial model to the work

Defined project

Professional Services & Consulting

Fixed fee or time and materials for a scoped outcome, implementation, acceptance, and handoff.

 Recurring capacity

Managed DevOps / Support & Advisory

Monthly business-hours engineering capacity against a shared Jira backlog.

 Clarify first

Assessment Programs

Use a focused diagnostic when the current state or right next step is not yet clear.
Related paths
Generative AI on AWSQualify and build generative AI applications on AWS with retrieval, agents, evaluation, human review, data controls, and observable failure handling. AWS Serverless ModernizationDesign and implement event-driven and serverless workloads with explicit state, integration, security, observability, cost, and failure boundaries. Build Generative AI on AWSTake a qualified generative AI use case from data and control boundaries through Bedrock architecture, evaluation, production delivery, and ownership.
FAQ

Scoping Intelligent Automation

Does AI work begin with model selection?

Not by default. It begins with the user, data, decision, failure cost, evaluation method, and whether probabilistic output is appropriate.

How is quality evaluated?

The scope defines representative test inputs, quality criteria, failure cases, human review, release thresholds, and production observations.

Who controls data and model access?

The client retains its accounts, data decisions, credentials, and provider agreements; the implementation documents permissions and operating ownership.

Bring the environment and the decision you are facing.

Use the free hour to work through the current state and identify a useful next step before you commit to a project.

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