The operations stack for e-commerce brands, built in weeks.

A dozen marketplace and ads APIs, scripts everywhere, spreadsheets on top. Every report is a hand-written query, every number has three versions, and nobody trusts any of them.

One data foundation. Raw data from every source lands with you, gets cleaned and conformed in layers, and is modelled into the marts your reports and tools read from. Scheduled, monitored, reported.

Dozens of accounts and marketplaces, and no off-the-shelf tool that shows the whole business the way you run it. So people check five dashboards and rebuild the view in a spreadsheet.

One window on top of the data foundation: P&L, sales and stock at every granularity, from the whole company down to a SKU on a marketplace, refreshed hourly.

Ads managed account by account, marketplace by marketplace, through consoles nobody enjoys. No view of ads next to margin and stock.

One interface for every ad account, on the same data as the P&L, with rules that adjust bids automatically.

Every planner did it their own way, with a new approach every month, in their own Excel. Overstocks and stockouts, and nobody tracked forecast accuracy.

One demand forecasting and planning platform and one method that everyone plans through.

Product data scattered across marketplace listings and spreadsheets. One product had five identifiers, different measurements and attributes in different places, and no owner.

A canonical product catalog (PIM): attributes, measurements, pricing, suppliers, files, identifiers per channel. One record, every system.

Suppliers were managed through email, WeChat and files. Order status lived in someone's head. A change in quantity or date took a week to reach the ERP and waited for someone to type it in.

One ERP-integrated tool where orders are negotiated, changed and tracked through the whole cycle, with no manual re-entry.

Finance used the ERP. Operations hated it and worked around it in spreadsheets. Under load, background jobs stalled.

Interfaces built for the people who do the work, with the ERP as the backbone underneath. The ERP moved to Kubernetes with autoscaling workers, heartbeats and dead-worker detection.

Changes to listings, yours or competitors', were noticed a week later, when sales had already dropped and nobody knew why.

A service that captures every change to the company's and competitors' products on Amazon: title, price, images, category, ownership.

Manual coding, ticket-to-code handoffs, features taking weeks.

Spec-driven, agent-executed development. Product owns specs, mockups and alignment. Engineering owns the agent harness. Architecture and accepted decisions (ADRs) live in the environment itself, so agents always build against them.

Agents can chat about the business but can't act on it safely. Every new interface meant re-implementing the same permissions and rules.

Business operations exposed through MCP, HTTP, CLI and agent entry points, sharing one set of authorization and business rules across all of them.

Managers spent their week chasing processes across tools: support tickets, meeting follow-ups, status questions, documentation. Every decision needed a person to carry it from the meeting into Jira, the spec and the docs.

One chat window where a manager runs the processes and agents do the work underneath. Incoming issues get triaged, answered and routed. A meeting ends in a transcript, a decision list, and agents that execute it: specs, prototypes, tickets. After every release, documentation and the announcement update themselves.