Product data cleanup
Merge duplicate records, standardize attributes and publish consistent content across storefront, marketplace and internal systems, with an audit trail for every change.
Industry
Software that keeps commerce moving.
Retail teams face pressure from every direction: thin margins, rising support volume, messy product data and customers who expect fast, accurate answers. We build software and AI that remove friction from the day-to-day.
Overview
Fewer handoffs, fewer surprises.
Commerce runs on a chain of small systems: catalog, inventory, orders, payments, shipping, support. When one link is manual or out of date, the cost shows up as refunds, late orders and frustrated customers. Most teams do not need a new platform. They need the existing one to work as a whole, with clean data and fewer handoffs. That is the work we take on. We fix the chain rather than the symptoms.
AI earns its place in retail when it shortens a real task: classifying a support email, writing a product description from attributes, checking a return against policy, or answering a stock question from live data. We wire those features into your systems with access control and review steps, and we test them on your own catalog and conversations before they reach a customer. The same approach applies to returns, fraud checks and post-purchase messaging.
Catalog data you can trust
We clean, enrich and connect product data across channels, so listings, search and recommendations stop contradicting each other.
Orders that do not need chasing
Order, payment and fulfillment flows with retries, clear failure states and alerts, so exceptions surface before customers call support.
Support with real answers
Assistants grounded in policy, order data and product details, with handover to a person when the question needs judgment.
Returns and fraud checks
Rules and models that flag unusual refunds or orders for review, while routine cases move through without a queue.
Peak traffic without drama
Caching, queues and load testing prepared ahead of seasonal spikes, with a rollback path if a release misbehaves.
Built for your team
Admin tools and dashboards your merchandising and operations staff can use without waiting on an engineer.
Use cases
Where commerce teams feel the pain
The same few problems show up across stores, marketplaces and retail brands.
Customer support assistant
Answer questions about delivery, sizing and returns from live order and policy data, then escalate anything unusual to an agent with context attached.
Inventory and pricing sync
Keep stock and price information aligned between warehouse, storefront and marketplaces, with scheduled jobs, alerts and a simple way to audit discrepancies.
Post-purchase automation
Automate tracking updates, review requests and exception handling so the period after checkout stays calm for both customers and staff.
How we work
How we approach retail systems.
Discover
We trace one customer path end to end, from product page to delivery, and find where data or manual work breaks it.
Design
We map the systems involved, agree on the smallest useful change and define the metrics that will show whether it worked.
Build
We deliver in short iterations against your storefront and back office, testing with real orders and real catalog data.
Support
We monitor launches through peak periods and leave your team with runbooks, dashboards and a clear support path.
FAQ
Common questions.
Will this work with our commerce platform?
Yes. We integrate with common commerce platforms, ERPs and warehouse systems through their APIs. Where an API is missing, we agree on a safe interim approach with your team.
Can you help without replacing our storefront?
In most cases, yes. We often start behind the storefront, improving data, integrations or support workflows before any customer-facing change.
How do you keep AI answers accurate for shoppers?
Assistants answer from your policy, product and order data rather than general knowledge. We test them against real questions and route uncertain cases to a person.
What happens during a busy season?
We avoid risky releases close to peak trading and prepare capacity and rollback plans in advance. If something does go wrong, we have a clear path back to the last known good state.
Next step
Start the conversation
Where is your commerce stack losing time?
Tell us which part of the order flow causes the most manual work. We will come back with a practical first step.