JING Tea is a premium single-garden tea brand founded in London in 2004. They sell through direct e-commerce, wholesale to hotels and restaurants, and international retail. We’ve worked with them on their financial and operational reporting for more than three years — across two different tech stacks.
When we started, their reporting was pieced together from several sources: Sage for accounting and inventory, extra inventory data from manual sources, and a hand-built budget. Nothing tied them together at the management level, so any question that crossed systems — profit by channel, stock versus plan, wholesale performance by product family — had to be assembled by hand. We helped them consolidate these sources into a single reporting layer, replacing the manual work with automated daily updates.
Last year the business changed its operating stack — Shopify for sales, Xero for accounting, CIN7 for inventory, warehousing, and operations. The usual route would have meant migrating years of history into the new systems. Instead, Onetribe’s Governed Data Layer kept old and new systems running side by side: Sage history stayed on Sage, new data flowed in from Shopify, Xero, and CIN7, and the reporting layer brought both into one model. No history moved. No reporting blackout. Budget versus actuals and drill-down from P&L to the individual sales order stayed available across both eras.
The same layer is now being extended with marketing data — connecting the path from first marketing touchpoint to booked revenue and gross margin . The data model is AI-ready, with Claude AI plugged in so the team can ask questions in plain English against numbers that already match across systems. The reporting layer that handled one full stack change without disruption will handle the next.
Context
- Premium single-garden tea brand founded in London in 2004
- Multi-channel operations: direct e-commerce, wholesale trade to hotels and restaurants, international retail
- Sources single-garden teas from China, India, Sri Lanka, Japan, and Taiwan
- Onetribe engaged 3+ years ago to build full financial and operational management reporting across multiple data sources
- Initial stack: Sage for accounting and inventory, with additional manual inventory inputs and manually assembled budgets
- Migrated last year to a new tech stack: Shopify (sales), Xero (accounting), CIN7 (inventory, warehousing, operations)
- Reporting layer currently being extended with a marketing data feed
- Data model is AI-ready; Claude AI integration live for ad-hoc analysis
Challenge
- Reporting spread across multiple disconnected sources — Sage accounting, Sage inventory, manual inventory inputs, manual budgets — with no unified layer to tie them together
- Any question that crossed systems had to be put together by hand before it could be answered
- The move to Shopify + Xero + CIN7 risked breaking reporting continuity
- The standard migration route would have meant rebuilding three-plus years of history in the new systems
- Finance and operations needed to keep reconciling performance across old and new during and after the switch
- The next step needed marketing data and AI-assisted analysis without adding more systems
What Was Implemented
- Direct integrations with all source systems, old and new: Sage (accounting + inventory), manual inventory inputs, manual budgets, then Shopify, Xero, and CIN7
- Proprietary Governed Data Layer as the single reporting foundation unifying legacy and new systems
- Standardized chart of accounts, product and SKU master, customer hierarchies, and channel taxonomy across both stacks
- Automated daily data extraction replacing manual inventory and budget consolidation
- Plan versus actuals reconciled in one model — variance analysis from group P&L down to individual sales order
- Multi-channel view combining direct e-commerce, wholesale trade, and retail in a single consolidated layer
- Phased migration support: old and new systems ran in parallel inside the reporting layer during cutover
- Historical data preserved on source systems — no historical data migration required
- Marketing data layer currently being added — extending the model from marketing touchpoint through to revenue and margin
- Claude AI plugged in for ad-hoc questions in plain English against governed, matched-up data
Outcomes
- Full management reporting maintained across three-plus years of operations and a complete tech stack change
- Historical data kept intact on source systems — no historical data migration required
- Sage-era history and Shopify/Xero/CIN7 current performance viewable side by side in one model
- Plan variance analysis and drill-down from financial headline to sales order available across both old and new data
- Multi-channel performance (e-commerce, wholesale, retail) unified into one consolidated view
- Reporting layer absorbed the tech stack change without additional rebuild cost or added complexity
- Marketing layer being added on the same foundation — future end-to-end view from marketing spend to financial impact
- AI-ready data model supporting Claude AI integration for natural-language analysis
- Infrastructure proven as a stable reporting layer across underlying system changes — Onetribe has become the standard multi-system reporting layer for the business