AI Automation + Commerce Systems for Consumer Brands

We 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.

Starting at $6,500

Typical projects: $12,500–$30,000+

Timeline: 4–8 weeks

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

About this service

Most ecommerce brands are adding AI in the wrong places.

Another chatbot.

Another content generator.

Another automation nobody asked for.

I start somewhere else:

Where is the brand losing revenue, customer attention or team time?

Then we decide whether AI can actually improve it.

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

ACQUIRE

Bring better customers in.

CONVERT

Help more interested customers buy.

RETAIN

Give customers better reasons to come back.

OPERATE

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.

The rule

Every system should do at least one of these:

MAKE MONEY

Increase qualified traffic.

Improve conversion opportunities.

Recover abandoned demand.

Improve merchandising.

Increase repeat purchase.

Help the team act faster on customer data.

SAVE TIME

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.

Ideally both.

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

01. ACQUIRE

Use AI to create better demand, not more noise

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

Example

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:

What changed.

Why it might matter.

What deserves attention today.

The team still makes the decision.

They just stop spending hours collecting the evidence.

02. CONVERT

Help customers choose faster

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:

What does the customer still need to understand before they are comfortable buying?

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.

03. RETAIN

Turn customer data into better repeat purchase

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.

04. CUSTOMER EXPERIENCE

Faster service without turning the brand into a bad chatbot

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.

05. MERCHANDISING + PRODUCT

Use AI where product operations become repetitive

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.

06. MARKETING + CREATIVE OPERATIONS

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.

07. COMMERCE OPERATIONS

Remove the work nobody should still be doing manually

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.

Platforms we can work around

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.

How the project works

Phase 1 — Find the money and time

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:

Money

Time

Customer attention

Information

Phase 2 — Build the opportunity map

Every AI opportunity is scored against:

Revenue potential

Can it realistically increase or recover revenue?

Time saved

Does it remove meaningful manual work?

Frequency

Does the problem happen every day?

Customer impact

Does it improve or damage the buying experience?

Data readiness

Do we have enough useful information?

Risk

What happens if the system gets something wrong?

Implementation effort

Is the expected return worth the build?

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

Phase 3 — Prioritize

We choose the systems with the strongest business case.

Not everything gets built.

Sometimes the most useful recommendation is:

Do not automate this yet.

That is still a good outcome.

Phase 4 — Architecture

For the selected systems, we define:

• Workflow

• Inputs

• Outputs

• Data

• AI models

• Integrations

• Human approvals

• Customer-facing behaviour

• Failure states

• Escalation

• Security/access

• Measurement

Phase 5 — Build + integrate

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

Phase 6 — Test

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

Phase 7 — Measure

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.

What you receive

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

Pricing

AI Commerce System

Starting at $12,500

Best for one meaningful revenue or operational problem.

AI Commerce Infrastructure

$20,000–$30,000+

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

Ongoing Optimization

$2,500–$5,000/month

Best fit

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.