The pain point in multi-channel retail isn't a lack of data — it's a data pipeline that keeps getting longer yet never gets used: manual pulls from platform back offices, hand-built reports, and dirty master data leaking in at the source. We use AI Agents to automate the whole chain — collect, store, compute, apply, and govern.
The more channels and platform back offices you run, the more these four issues drag down your data team and business teams together.
Data from multiple e-commerce platform back offices has to be logged into, exported, and cleaned up by hand: sessions expire, pages get redesigned, and metrics are defined differently — a single pull takes half a day and still comes back with errors.
Operations, merchandising, and supply chain all want "fully covered" daily and weekly reports. On the traditional BI route of modeling and dashboard building, the data team can never catch up — and requirements keep changing daily.
Product, supplier, and material master data in ERP / SCM / order systems is keyed in by hand, and operating standards written on paper can't control what gets typed — once master data is dirty, everything computed or derived from it is wrong.
Even with BI tools deployed, business users still can't get their own data: they can't read the dimensions and metrics, requests go into a queue, and getting data means chasing people. Switching platforms won't close this gap.
No need to launch everything at once: prove value with one or two lighthouse use cases first, then extend across the full chain.
An AI data-collection agent solves "reliably bringing the data back": login sessions are kept alive automatically, page redesigns are adapted to on the fly, and post-collection reconciliation and metric checks run automatically — the data arrives ready to use, with no second round of manual cleaning.
Business users ask for data in plain language: "Why did GMV drop day-over-day in that channel yesterday?" The AI handles retrieval, attribution, conclusions, and charts — no need to build a dashboard for every role.
Data-entry operating standards become a real-time validation agent: required fields, coding rules, and referential integrity — errors are blocked the moment they're typed. Enforceable digital discipline replaces paper SOPs.
Aggregates orders, return and exchange line items, service records, and QC feedback, then outputs return-rate attribution by product (spec / quality / logistics / not-as-described), a problem-SKU list, and improvement recommendations.
Combines industry data, on-site search, and review keywords to deliver weekly and quarterly trend reports and opportunity signals, moving merchandise planning from "rear-view reporting" to forward-looking forecasting.
We don't compete with your BI tools: a semantic layer unifies metric definitions, agents unify the interaction, and a knowledge base accumulates use cases. If the underlying tools are replaced later, the AI assets keep running — protecting your existing investment.
On-site co-creation, with acceptance-tested deliverables at every stage; if any step fails to show value, you can simply stop at the previous one.
Pick the one or two highest-value use cases (usually returns diagnostics or conversational data Q&A), connect real data, and deliver a demo-ready result.
Turn intelligent collection from platform back offices and connections to ERP / SCM / order systems into stable data sources agents can use; operating standards are batch-converted into executable validation rules.
Roll conversational analytics out to business leads, establish an operating model of "business proposes use cases, both sides co-create and launch together," and make coverage and labor-hour savings measurable.
Tell us about your channel mix and the one data problem that hurts most — we'll come back with use-case recommendations and an assembly plan.
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