Industries / Ecommerce
Help shoppers find the right product.
Give customers helpful search and recommendations based on accurate product information. We plan and build with your team, measure what changes and keep people in control.
Your account executive is your dedicated point of contact throughout the project.
The challenge
When shopping feels harder than it should.
Customers lose trust when product information is incomplete, recommendations are unavailable or no one can tell what is working.
01 / Catalog data is not ready
Missing product details, inconsistent prices and outdated stock can make even a helpful search tool give a poor answer. We start by finding and fixing the gaps that affect customers.
02 / Recommendations point at what you cannot sell
A model that suggests an out-of-stock or discontinued item burns the exact trust that drives conversion.
03 / Personalization runs into privacy constraints
Personal experiences depend on customer data. We help you use that data responsibly and make consent part of the design.
04 / Nobody can attribute the lift
If you cannot compare results before and after launch, you cannot tell whether the new experience helped. We agree on the measures first.
How we help
A better path from search to sale.
We connect product data, customer experience and measurement so the experience is useful for shoppers and accountable to your team.
Product answers grounded in your catalog
Help customers discover relevant products while checking live price and availability before anything is recommended.
Ready for new ways to shop
Prepare accurate product information, pricing, availability and checkout rules for AI shopping tools. We assess the channels that matter to your store and plan integrations around their current requirements.
Tests based on real shopping questions
Test common customer questions, product availability and answer quality against a standard agreed before launch.
A clear way to measure results
Agree in advance how to measure changes in sales, order value and repeat purchasing.
Proof / Optagen
See the thinking behind Optagen.
Optagen is our own live product, built to help store operators decide what needs attention.
The problem
An e-commerce operator has store data, analytics, inventory, and marketing in four or five systems, and no system can see the thing that matters most. A product drives strong marketing revenue and still loses money once returns and ad spend are accounted for. Every dashboard shows it winning. The margin is gone anyway.
The design
Not another dashboard. A decision layer that reads across the connected systems and ranks what to fix by impact, urgency, and confidence. Three decisions did the work.
Correlate before recommending
Repeated sizing complaints, return rate, contribution margin, paid traffic, and product-page context are joined before anything is proposed. Recommending more spend on a product that loses money after returns is the failure mode the architecture exists to prevent.
Show the evidence, always
Every recommendation carries its expected impact, the systems it touches, a confidence score, and a link to the underlying evidence. A ranked list nobody can audit is just a more confident dashboard.
Stage the autonomy
Three modes, chosen per action type by the operator: manual, agent-assisted, and automated within rules they define. The load-bearing promise is one sentence: nothing changes without your approval. Every other decision in the system is downstream of keeping it.
What handling the data actually looked like
The public Optagen demo shows sources with different levels of completeness and different sync states. Those figures are explicitly illustrative sample data. They explain why the design shows missing information; they are not measured results from client stores.
One detail worth copying
The demo data on the product site is labelled "illustrative sample store data, modeled on the Optagen product." It would have been easier to leave that off. We would rather ship a product that tells you which numbers are real.
What it is now
Optagen is a live product with public plans and supported integrations listed on its site. Review the current product details to see how it fits your store.
Product evidence
Explore the public product to see how recommendations, evidence and approval choices are presented. The sample store figures are labelled as illustrative.
Facing a similar challenge? Tell us what is happening. We will help you find the right next step.
Talk to usCommon questions
Questions commerce teams ask.
Our platform already has recommendations.
We start by checking how well those recommendations serve customers and whether you can measure the result. If your platform already meets the need, we will say so.
Can our platform already do this?
Possibly. We start by checking what your platform already does, where product data or measurement falls short and whether a new build would add value.
Can we begin without a long discovery project?
Yes. We begin with a focused conversation, then recommend the smallest useful next step.
Who we work with
Is this the right fit?
A good fit
Commerce teams that want customers to find relevant, available products and want clear evidence of what improves sales.
A different starting point
If the main issue is inaccurate product data, we can help you identify it before recommending a new AI experience.
Explore our services
A clear next step for your project.
Find a practical starting point for AI, build a workflow with your team, or strengthen your applications and AI systems.
Let's talk
A real person, from day one.
Tell us what you want to achieve. Your account executive will be your dedicated point of contact, helping you choose the right next step and keeping the work connected to your team. See how we work together.
- Your contact
- An account executive who knows your goals and stays involved throughout the engagement.
- Your team
- The people who use and run the solution help shape it from the start.
- Your outcome
- A clear recommendation and work your team can understand, own and build on.
Or email hello@aicoop.co with one question.