I design and implement AI systems for ecommerce and consumer brands that help increase revenue, improve customer experience and remove repetitive operational work.

From acquisition and merchandising to retention, support, reporting and internal workflows, every system has to create a believable commercial return.

Typical projects: $12,500–$30,000+

Timeline: 4–8 weeks

Optional ongoing optimization: $2,500–$5,000/month

Most ecommerce brands are adding AI in the wrong places.

Another chatbot.

Another content generator.

Another automation nobody asked for.

I start somewhere else:

Then we decide whether AI can actually improve it.

For consumer brands, the biggest opportunities usually sit across four parts of the business:

Bring better customers in.

Help more interested customers buy.

Give customers better reasons to come back.

Remove repetitive work behind the scenes.

The goal is not to make your company “AI-powered.”

The goal is to make the business more profitable, responsive and easier to operate.

Every system should do at least one of these:

Increase qualified traffic.

Improve conversion opportunities.

Recover abandoned demand.

Improve merchandising.

Increase repeat purchase.

Help the team act faster on customer data.

Reduce manual reporting.

Remove repetitive merchandising work.

Reduce support workload.

Automate product and catalog operations.

Speed up creative and marketing workflows.

Connect systems that currently require people to move information manually.

If an AI system looks impressive but has no credible commercial case, I would rather not build it.

Possible systems include:

Audience and customer research workflows

Creative research assistants

Competitor monitoring

Ad-performance intelligence

Creative fatigue detection

Customer-review mining

Product-demand signals

Campaign reporting

Automated creative briefs

Influencer/creator prospecting

Partnership research

Wholesale prospecting

Lead enrichment

Campaign opportunity alerts

Instead of your marketing team manually checking Meta, Google, Pinterest, Shopify, reviews and competitors every morning, a system can pull the relevant signals together and show:

The team still makes the decision.

They just stop spending hours collecting the evidence.

This is one of the most interesting areas for consumer brands.

Possible systems include:

AI shopping assistants

Product recommendation systems

Guided product finders

Intelligent search

Product comparison assistants

Pre-purchase support

Personalized product education

Customer-objection handling

Review summarization

Product-fit recommendations

Cross-sell and upsell systems

Cart-recovery workflows

Merchandising support

Personalized landing experiences

The question is:

Then we build around that friction.

For fashion, that might be fit, styling or sizing.

For beauty, it might be product matching, routine building or ingredients.

For wellness, it may be education and product selection.

For jewelry or luxury, it may be trust, comparison, materials and buying confidence.

AI becomes useful when it helps the customer make a better decision.

Most brands have more customer information than they know how to use.

Possible systems include:

Customer segmentation

VIP detection

Win-back workflows

Churn-risk detection

Replenishment reminders

Next-product recommendations

Personalized post-purchase communication

Review requests

Referral systems

Loyalty intelligence

Customer-feedback analysis

Support-history analysis

Retention opportunity alerts

Product affinity analysis

Instead of sending the same campaign to everyone, the brand can act differently based on what customers actually bought, asked, returned, liked or ignored.

Possible systems include:

Customer-service triage

AI support assistants

Order-status assistance

Product-question support

Returns/exchange routing

Support-ticket categorization

Conversation summaries

Escalation logic

Customer sentiment tracking

FAQ intelligence

Support knowledge bases

Agent-assist systems

The objective is not to eliminate people from customer service.

It is to stop people wasting time answering the same predictable questions while making sure complicated or sensitive issues still reach a human.

Possible systems include:

Product-data enrichment

Product-description workflows

Attribute cleanup

Product tagging

Collection logic

Merchandising assistance

Search synonym generation

Review-to-product insight

Catalog QA

Product-launch workflows

Product image/content coordination

Recommendation logic

Inventory-based merchandising alerts

For brands with large catalogs, this can remove a surprising amount of repetitive work.

This is where my background becomes especially useful.

Possible systems include:

Creative-performance reporting

Winning-angle detection

Customer-language mining

Review-to-creative workflows

Creative briefing

Campaign summaries

Editorial/content research

Competitor monitoring

UGC categorization

Influencer-content analysis

Campaign knowledge bases

Automated reporting

Creative asset organization

Performance-to-creative feedback loops

The goal is not AI-generated creative for the sake of volume.

The goal is helping the creative and growth teams make better decisions faster.

Potential systems include:

Shopify reporting

Daily business summaries

Order anomaly alerts

Inventory alerts

Returns analysis

Refund trend analysis

Margin reporting

Product-performance summaries

Customer-service reporting

Forecasting support

Vendor workflows

Catalog operations

Data synchronization

Operations dashboards

Internal knowledge assistants

SOP assistants

Cross-team handoffs

If a person spends hours every week copying data between Shopify, spreadsheets, ads platforms, email tools and Slack, that is usually a good place to investigate.

Depending on your stack:

Shopify

Shopify Plus

BigCommerce

Klaviyo

Meta

Google Ads

GA4

Pinterest

TikTok

Gorgias

Zendesk

Recharge

Yotpo

Attentive

HubSpot

Salesforce

Google Workspace

Slack

Airtable

Notion

n8n

Make

Zapier

OpenAI

Claude

APIs

Custom applications

The stack follows the problem.

Not the other way around.

We audit the commerce business across:

Acquisition

Storefront

Products

Customer journey

Retention

Support

Marketing operations

Merchandising

Reporting

Internal operations

Then we identify where the business is losing:

Every AI opportunity is scored against:

Can it realistically increase or recover revenue?

Does it remove meaningful manual work?

Does the problem happen every day?

Does it improve or damage the buying experience?

Do we have enough useful information?

What happens if the system gets something wrong?

Is the expected return worth the build?

This stops the project becoming a pile of random AI experiments.

We choose the systems with the strongest business case.

Not everything gets built.

Sometimes the most useful recommendation is:

That is still a good outcome.

For the selected systems, we define:

Workflow

Inputs

Outputs

Data

AI models

Integrations

Human approvals

Customer-facing behaviour

Failure states

Escalation

Security/access

Measurement

The system is implemented around your existing commerce stack wherever possible.

That may include:

AI agents

Automation workflows

APIs

Shopify integrations

Customer-data workflows

Reporting systems

Internal dashboards

Knowledge systems

Custom interfaces

Databases

Webhooks

CRM/lifecycle integrations

Before deployment, we test:

Accuracy

Edge cases

Bad inputs

Missing data

Customer-facing responses

Human handoffs

Duplicate events

Failure handling

Security/access

Business logic

Cost

Reliability

We define the intended commercial impact before launch.

Depending on the system, that might include:

Conversion rate

Revenue recovered

AOV

Repeat purchase

Support cost

Response time

Hours saved

Creative reporting time

Merchandising workload

Retention rate

Customer-service volume

Catalog processing time

Team adoption

If we cannot explain what success looks like, we should not build it.

Depending on scope:

Commerce AI audit

Opportunity map

Prioritized roadmap

Business-case estimates

System architecture

Workflow design

AI/automation implementation

Integrations

Custom logic

Testing

Documentation

Team training

Measurement framework

30-day post-launch review

Best for one meaningful revenue or operational problem.

For multiple connected systems across acquisition, conversion, retention or operations.

Designed specifically for:

Fashion

Beauty

Jewelry

Wellness

Lifestyle

Luxury

DTC brands

Ecommerce brands

Retail + ecommerce brands

Consumer products

Subscription commerce

Every system should increase revenue, protect revenue, improve customer experience or save meaningful time. Otherwise, we don’t build it.