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Use cases

Our work. What changed.

See the problem, the system, and the evidence: from finding stock across locations to turning one content brief into posts for three platforms.

Client-reported figures are labelled. Each example explains what is measured and what still needs validation.

Explore the projects ↓

Find a workflow like yours

These examples describe existing work. Clients are anonymised; reported results and measurement limits are shown with each project. Use them to explore an approach for your workflow.

6 projects shown

Automotive operations

Turning hidden inventory into available cash

The problem

Parts were difficult to locate, employees lost time searching, and purchasing did not reflect local demand.

What we built

An AI inventory and reporting system with searchable stock and location-specific demand prediction.

The result

  • Client-reported result

    50%+ — improvement in available cash flow

    within six months

  • Client-reported result

    ~30 min — saved per employee, per day

    daily, across search and retrieval tasks

Evidence & limits

Cash-flow and time-savings figures are client-reported. Source records and measurement definitions are being documented before this story becomes a full case study.

Operational automation · Predictive reporting · Custom software

Psychology clinic

Opening new acquisition channels

The problem

The clinic relied on referrals and word of mouth; prospective clients searching online could not find or easily contact the practice.

What we built

A modern website built for search and AI-assisted discovery, with a streamlined path from enquiry to appointment.

The result

  • Client-reported result

    New enquiries — from prospective clients, through online channels the clinic did not have before

    since launch

Evidence & limits

Enquiry volume is client-reported. Channel attribution and conversion tracking are still being set up before specific figures are published.

Websites · AIO + SEO · Workflow automation

SaaS · Customer success

Keeping customer knowledge inside the company

The problem

Account knowledge lived in individual inboxes and heads. When people were away or moved on, customer context left with them.

What we built

An AI knowledge system that captures customer conversations and makes account history searchable for the whole team.

The result

  • Client-reported result

    Retained — customer context stays with the company through handovers, holidays, and departures

Evidence & limits

Outcomes are client-reported and qualitative today. Response-time and onboarding measurements are being defined so this story can carry hard numbers.

AI agents · Knowledge management

Event planning

From invisible to discoverable

The problem

The company barely appeared in search results, so new business depended almost entirely on word of mouth.

What we built

A rebuilt web presence on an AIO + SEO foundation, structured around the services clients actually search for.

The result

  • Operational change

    Discoverable — the company now appears where clients search, and generates organic leads

Evidence & limits

Search visibility and lead-source tracking are in place; lead counts will be published once a full measurement period has accumulated.

AIO + SEO · Websites

Food industry

Turning reviews into a lasting asset

The problem

Customer praise vanished into passing conversations, and every menu or promotion change meant another round of printing.

What we built

A digital menu and review system the business updates itself: feedback is collected and kept, and changes go live without reprinting.

The result

  • Operational change

    Lower recurring costs — menu and promotion updates ship digitally, reducing repeat printing costs

Evidence & limits

The printing-cost reduction is directional and structural; a monetary figure will be stated only once a full cost comparison is documented.

Websites · Content systems

Fitness

One content workflow for three platforms

The problem

Every platform needed its own posts, so publishing consistently meant redoing the same work three times.

What we built

A single AI-assisted content workflow that turns one input into platform-ready posts for three channels.

The result

  • Operational change

    1 → 3 — one workflow now supports three platforms

Evidence & limits

The structural change (one workflow, three platforms) is in production. Time and reach measurements are being collected before any savings are stated.

Content systems · AI agents

How AureliaEdge works

Diagnose. Build. Measure. Scale.

01
Diagnose

Find the one bottleneck that measurably slows the business down.

02
Build

Ship the simplest useful system that removes it.

03
Measure

Check what actually changed before expanding anything.

04
Scale

Extend only the workflows that proved their value.

We start with one measurable bottleneck, not a broad AI transformation project.

Where is your business losing time or money?

Your first useful AI system may already be hiding inside a workflow your team repeats every day.

Discuss your project coverage

Ask us about coverage for your project.

Frequently asked questions

How should I use these automation examples?

Use the examples to identify a similar bottleneck in your own operation, such as repetitive work, disconnected reporting, or knowledge that is difficult to retrieve. The workflow and its constraints matter more than choosing the same tool.

Will my business achieve the same outcome?

Outcomes depend on the process, available data, integrations, and team adoption. Define a baseline and success measures for your workflow before implementation, then evaluate the first automation before expanding.

What is the next step for a similar workflow?

Start with the Operations Audit or contact AureliaEdge with a description of the process. Include the tools involved, who owns each step, and where delays or repeated manual work appear. That gives us a concrete starting point for scoping a first sprint.