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AT ATIENZA TEAM
Services

Four ways to start a project.

Each one is scoped around a business problem rather than a list of tools. Timing and pricing depend on scope — we set both in writing before anything starts.

01

AI Opportunity & Workflow Audit

BEST FIT You suspect AI could help but do not yet know where it pays off.

The problem. Manual work is spread across tools and people, and nobody has mapped which parts are worth automating or what it would take.

What we do. We map the workflows end to end, size the manual effort, and come back with a prioritised list of what to automate, what to leave alone, and what has to be fixed first.

02

AI Automation Sprint

BEST FIT One workflow is clearly costing time and you want it fixed, not studied.

The problem. A specific repetitive process eats hours every week, and errors surface too late to correct cheaply.

What we do. We design and implement that one workflow properly — including validation, exception handling, human-review checkpoints, documentation, and handover to your team.

03

Internal AI Operations System

BEST FIT The problem is bigger than one workflow — it needs an application or a system of record.

The problem. Work lives in spreadsheets and chat threads, so nobody can answer what is in flight, who owns it, or what is waiting on review.

What we do. We build the internal application, dashboard, knowledge system, or multi-step operational workflow, with the approval gates and reporting the operation actually needs.

04

Ongoing AI Operations Partner

BEST FIT Something is already live and you do not want it quietly rotting.

The problem. Automations drift as tools, teams, and volumes change, and nobody owns monitoring, exceptions, or documentation after launch.

What we do. We monitor what is running, work through exceptions, keep documentation current, and extend the system as the operation changes.

How a project actually runs.

Five stages, each with an owner and an exit condition. Click a number to focus a stage.

Discover

Interviews and walkthroughs with the people who run the work today, not just the people who describe it.

Exit: agreed problem statement and success criteria.

Map and prioritise

Current-state map, manual effort sized, opportunities ranked by value against effort and risk.

Exit: a prioritised scope you have signed off.

Design and build

Rules, data model, and workflow designed in writing, then built in increments you review as they land.

Exit: working system with rules implemented as agreed.

Validate with human review

Test scenarios for the awkward cases, plus the approval gates that keep automated output from acting unchecked.

Exit: exception handling proven against real edge cases.

Launch, document, improve

Launch with operating procedures and training, then refine against what actually happens in use.

Exit: your team can run it without us.

Have a workflow in mind? Let's map what a practical implementation could look like.

Plan an AI project